NAMIC Wiki - User contributions [en]

People

2019-11-11T20:24:28Z

Anry:

== Personnel at least partially funded by NA-MIC ==

#'''Leadership Core'''
## '''[[User:Kikinis|Ron Kikinis]], Harvard (BWH SPL) PI'''
## [[User:Marianna|Marianna Jakab]], Harvard (BWH SPL)
## [[User:Mastrogiacom|Katie Mastrogiacomo]], Harvard (BWH SPL)
# '''Algorithms Core'''
## University of Utah C1
###'''[http://www.cs.utah.edu/~whitaker/ Ross Whitaker], PI'''
### Manasi Datar
### Samuel Gerber
### Yongshen Pan
## University of Utah
### '''[http://www.sci.utah.edu/~gerig/ Guido Gerig], PI'''
### Emmanuel Bitaud
### Stanley Dirrleman
### James Fishbaugh
### Marcel Prastawa
### [[User:Gcasey|Casey Goodlett]]
### Preston T. Fletcher
### Ran Tao
## MIT (CSAIL)
### '''[[Polina_Golland|Polina Golland]], PI'''
### [[Eric_Grimson|Eric Grimson]]
### [[User:FernD|Fern DeOliveira]]
### Michal Depa
### Tammy Riklin Raviv
## UNC
### '''[[User:Styner|Martin Styner]], PI'''
### [http://www.med.unc.edu/psych/directories/hazlett.htm/ Heather Cody Hazlett]
### Michael Garret Larson
### Gary Long
### Deepika Mahalingam
### Ipek Oguz
### Rachel Gimpel Smith
### Clement Vachet
## Stony Brook University
### '''[http://iss.bu.edu/tannenba/ Allen Tannenbaum], PI'''
### Yi Gao
### '''[https://sites.uab.edu/anry/ Arie Nakhmani]'''
### Ivan Kolesov
### Peter Karasev
## MGH
### [http://www.nmr.mgh.harvard.edu/martinos/people/showPerson.php?people_id=56 Bruce Fischl]
## Kitware, Inc.
###'''[[User:Will| Will Schroeder]], PI'''
### [[User:Barre|Sebastien Barre]]
### [http://www.kitware.com/profile/team/hoffman.html/ William Hoffman]
### [[User:Ibanez|Luis Ibanez]]
## GE
### '''[[User:Millerjv|Jim Miller]], PI'''
### Roshni Bhagalia
### Dirk Padfield
### James Ross
### [[User:Taox|Xiaodong Tao]]
### [[User:Harveerar|Harini Veeraraghavan]]
### [[User:kedar_p|Kedar Patwardhan]]
## Isomics
### '''[[User:Pieper|Steve Pieper]], PI'''
### Alex Yarmakovich
## WUSTL
### '''[http://nrg.wustl.edu Daniel Marcus], PI'''
### Kevin Archie
### Mikhail Milchenko
### Timothy Olsen
## UCSD
### '''Jeffrey S. Grethe, PI'''
### Mark Ellisman
### Marco Ruiz
# '''Driving Biological Projects (DBP)'''
## University of Utah
### '''[http://www.sci.utah.edu/~macleod/ Rob MacLeod], PI'''
### Josh Blauer
### Josh Cates
## Iowa
### '''Hans Johnson, PI'''
### Norman Williams
### [[User:Dmwelch | Dave Welch]]
### Eun Young "Regina" Kim
### Joy Matsui
### Ali Ghayhoor
### Chen Yang
## MGH
### '''Gregory C. Sharp, PI'''
### Rui Li
## UCLA
### '''Jack Van Horn, PI'''
### Jeffrey Alger
### Ian Bowman
### Celia Cheung
### Nathan Hageman
### David Hovda
### Arthur W. Toga
### Paul Vespa
## Queen's University
### '''[http://www.cisst.org/~gabor/ Gabor Fichtinger], PI'''
### [http://media.cs.queensu.ca/purang/ Purang Abolmaesumi]
### [http://imaging.robarts.ca/~dgobbi/ David Gobbi]
### Siddharth Vikal
## The Mind Institute
### '''[http://www.mrn.org/principal-investigators/h-jeremy-bockholt Jeremy Bockholt], PI'''
### Mark Scully
## BWH
### '''[http://lmi.bwh.harvard.edu/~kubicki/ Marek Kubicki], PI'''
### Jorge Alvarado
### Jennifer Goodrich
### Padmapriya Srinivazan
# '''Service Core'''
## Kitware, Inc.
### '''[[User:Will|Will Schroeder]], PI'''
### Zack Galbreath
### William Hoffman
### Julien Jomier
### Zach Mullen
# '''Training Core'''
## '''[[User:SPujol|Sonia Pujol]], Harvard (BWH SPL) PI'''
## [[User:Randy|Randy Gollub]], Harvard (MGH)
# '''Dissemination Core'''
## '''[[User:Tkapur|Tina Kapur]], Harvard (BWH SPL) co-PI'''
## '''[[User:Pieper|Steve Pieper]], Isomics co-PI'''
# '''Administration Core'''
## Rachana Manandhar, Harvard (BWH SPL)
## [[User:Sanjay|Sanjay Manandhar]], Harvard (BWH SPL)

== NA-MIC Collaborators ==
These NA-MIC collaborators are funded under the "Collaboration with NCBC" PAR.
#Nicole Grosland, UIowa
#Vincent Magnotta, UIowa
#Steve Pieper, Isomics
#James Daunais, Wake Forest
#Robert Kraft, Wake Forest
#Chris Wyatt, Virginia Tech
#Kilian Pohl, Harvard (BWH SPL)
#Sandy Wells, Harvard (BWH SPL)
#Kevin Cleary, Georgetown
#Enrique Campos-Nanez, George Washington U.
#Patrick (Peng) Cheng, Georgetown
#Ziv Yaniv, Georgetown
#Nobuhiko Hata, Harvard (BWH)
#Curtis Lisle, KnowledgeVis

==NA-MIC EAB==

Our External Advisory Board members are listed [[EAB|here]].

== NA-MIC alumni ==
*[[User:Naucoin|Nicole Aucoin]], BWH, SPL
*Wendy Plesniak, Harvard, BWH, SPL
* [http://marchingcubes.org Bill Lorensen]
* [[User:Lzollei|Lilla Zollei]], MIT (CSAIL)
* Lauren O'Donnell, MIT (CSAIL)
* [http://people.csail.mit.edu/wanmei/ Wanmei Ou], MIT (CSAIL)
* [[Mahnaz_Maddah|Mahnaz Maddah]], MIT (CSAIL)
* Ramsey Al-Hakim, Georgia Tech
* [[User:Melonakos|John Melonakos]], Georgia Tech
* [[User:Lankton|Shawn Lankton]], Georgia Tech
* [[User:Nain|Delphine Nain]], Georgia Tech
* Xavier Le Faucheur, Georgia Tech
* [[User:Mohan|Vandana Mohan]], Georgia Tech
* Tom Fletcher, Utah
* [http://www.sci.utah.edu/cgi-bin/SCIpersonnel.pl?username=tolga Tolga Tasdizen], Utah
* [http://www.cs.utah.edu/~sbasu/ Saurav Basu], Utah
* Josh Snyder, Harvard (MGH)
* [[User:DavidTuch|David Tuch]], Harvard (MGH)
* [[User:Karthik|Karthik Krishnan]],Kitware
* [http://www.kitware.com/profile/team/cedilnik.html/ Andy Cedilnik], Kitware
* [[User:Mathieu|Mathieu Malaterre]], Kitware
* [http://www.stat.ucla.edu/~dinov/ Ivo Dinov], UCLA
* [[User:MichaelPan|Michael Pan]], UCLA
* Brendan Flaherty, UCSD
* [[User:Adamc|Adam Cohen]], Harvard (BWH PNL)
* [[User:Markd|Mark Dreusicke]], Harvard (BWH PNL)
* Martha Shenton, Harvard (BWH PNL) PI
* [http://lmi.bwh.harvard.edu/~sylvain/ Sylvain Bouix], Harvard (BWH PNL)
* [http://lmi.bwh.harvard.edu/~marc/ Marc Niethammer], Harvard (BWH PNL)
* [http://synapse.hitchcock.org/bios/saykin.shtml Andy Saykin], Dartmouth PI
* Bob Roth, Dartmouth
* [http://synapse.hitchcock.org/bios/flashman.shtml Laura Flashman], Dartmouth
* [http://www.dhmc.org/providers/dhmc_provider_634.html Thomas McAllister], Dartmouth
* Alan Green, Dartmouth
* John West, Dartmouth
* [http://synapse.hitchcock.org/bios/mchugh.shtml/ Tara McHugh], Dartmouth
* [http://synapse.hitchcock.org/bios/pixley.shtml Heather Pixley], Dartmouth
* Stephen Guerin, Dartmouth
* John MacDonald, Dartmouth
* [http://www.bic.uci.edu/faculty/sgpotkin.htm/ Steve Potkin], UCI PI
* [[User:Jfallon|James Fallon]], UCI
* Jessica Turner, UCI
* Martina Panzenboeck, UCI
* David Medina, UCI
* [http://www.ics.uci.edu/~smyth/ Padhraic Smyth], UCI
* [http://www.ics.uci.edu/~sternh/ Hal Stern], UCI
* Diane Highum, UCI
* [http://www.ess.uci.edu/~yu/ Yi Jin], UCI
* Liv Trondsen, UCI
* Fabio Macciardi, Toronto
* [http://www.utpsychiatry.ca/dirsearch.asp?id=130 Jim Kennedy], Toronto
* Aristotle Voineskos, Toronto
* [http://kotaro.naist.jp/~meg/eindex.html Megumi Nakao], NAIST

== "Friends and Family" ==

=== NIH ===

* [[User:Lysterp|Peter M. Lyster]]

=== mBIRN ===

* [[User:Akolasny|Anthony Kolasny]]
* [[User:Dmarcus|Dan Marcus]]
* [[User:Kikinis|Ron Kikinis]]

=== fBIRN ===

* [[User:Kikinis|Ron Kikinis]]

=== IGT ===

* [[User:Ibanez|Luis Ibanez]]
* [[User:Noby| Nobuhiko Hata]]

=== Other ===

People

2019-11-11T20:24:03Z

Anry: Added link to Arie Nakhmani's lab

== Personnel at least partially funded by NA-MIC ==

#'''Leadership Core'''
## '''[[User:Kikinis|Ron Kikinis]], Harvard (BWH SPL) PI'''
## [[User:Marianna|Marianna Jakab]], Harvard (BWH SPL)
## [[User:Mastrogiacom|Katie Mastrogiacomo]], Harvard (BWH SPL)
# '''Algorithms Core'''
## University of Utah C1
###'''[http://www.cs.utah.edu/~whitaker/ Ross Whitaker], PI'''
### Manasi Datar
### Samuel Gerber
### Yongshen Pan
## University of Utah
### '''[http://www.sci.utah.edu/~gerig/ Guido Gerig], PI'''
### Emmanuel Bitaud
### Stanley Dirrleman
### James Fishbaugh
### Marcel Prastawa
### [[User:Gcasey|Casey Goodlett]]
### Preston T. Fletcher
### Ran Tao
## MIT (CSAIL)
### '''[[Polina_Golland|Polina Golland]], PI'''
### [[Eric_Grimson|Eric Grimson]]
### [[User:FernD|Fern DeOliveira]]
### Michal Depa
### Tammy Riklin Raviv
## UNC
### '''[[User:Styner|Martin Styner]], PI'''
### [http://www.med.unc.edu/psych/directories/hazlett.htm/ Heather Cody Hazlett]
### Michael Garret Larson
### Gary Long
### Deepika Mahalingam
### Ipek Oguz
### Rachel Gimpel Smith
### Clement Vachet
## Stony Brook University
### '''[http://iss.bu.edu/tannenba/ Allen Tannenbaum], PI'''
### Yi Gao
### '''[https://sites.uab.edu/anry/ Arie Nakhmani], PI'''
### Ivan Kolesov
### Peter Karasev
## MGH
### [http://www.nmr.mgh.harvard.edu/martinos/people/showPerson.php?people_id=56 Bruce Fischl]
## Kitware, Inc.
###'''[[User:Will| Will Schroeder]], PI'''
### [[User:Barre|Sebastien Barre]]
### [http://www.kitware.com/profile/team/hoffman.html/ William Hoffman]
### [[User:Ibanez|Luis Ibanez]]
## GE
### '''[[User:Millerjv|Jim Miller]], PI'''
### Roshni Bhagalia
### Dirk Padfield
### James Ross
### [[User:Taox|Xiaodong Tao]]
### [[User:Harveerar|Harini Veeraraghavan]]
### [[User:kedar_p|Kedar Patwardhan]]
## Isomics
### '''[[User:Pieper|Steve Pieper]], PI'''
### Alex Yarmakovich
## WUSTL
### '''[http://nrg.wustl.edu Daniel Marcus], PI'''
### Kevin Archie
### Mikhail Milchenko
### Timothy Olsen
## UCSD
### '''Jeffrey S. Grethe, PI'''
### Mark Ellisman
### Marco Ruiz
# '''Driving Biological Projects (DBP)'''
## University of Utah
### '''[http://www.sci.utah.edu/~macleod/ Rob MacLeod], PI'''
### Josh Blauer
### Josh Cates
## Iowa
### '''Hans Johnson, PI'''
### Norman Williams
### [[User:Dmwelch | Dave Welch]]
### Eun Young "Regina" Kim
### Joy Matsui
### Ali Ghayhoor
### Chen Yang
## MGH
### '''Gregory C. Sharp, PI'''
### Rui Li
## UCLA
### '''Jack Van Horn, PI'''
### Jeffrey Alger
### Ian Bowman
### Celia Cheung
### Nathan Hageman
### David Hovda
### Arthur W. Toga
### Paul Vespa
## Queen's University
### '''[http://www.cisst.org/~gabor/ Gabor Fichtinger], PI'''
### [http://media.cs.queensu.ca/purang/ Purang Abolmaesumi]
### [http://imaging.robarts.ca/~dgobbi/ David Gobbi]
### Siddharth Vikal
## The Mind Institute
### '''[http://www.mrn.org/principal-investigators/h-jeremy-bockholt Jeremy Bockholt], PI'''
### Mark Scully
## BWH
### '''[http://lmi.bwh.harvard.edu/~kubicki/ Marek Kubicki], PI'''
### Jorge Alvarado
### Jennifer Goodrich
### Padmapriya Srinivazan
# '''Service Core'''
## Kitware, Inc.
### '''[[User:Will|Will Schroeder]], PI'''
### Zack Galbreath
### William Hoffman
### Julien Jomier
### Zach Mullen
# '''Training Core'''
## '''[[User:SPujol|Sonia Pujol]], Harvard (BWH SPL) PI'''
## [[User:Randy|Randy Gollub]], Harvard (MGH)
# '''Dissemination Core'''
## '''[[User:Tkapur|Tina Kapur]], Harvard (BWH SPL) co-PI'''
## '''[[User:Pieper|Steve Pieper]], Isomics co-PI'''
# '''Administration Core'''
## Rachana Manandhar, Harvard (BWH SPL)
## [[User:Sanjay|Sanjay Manandhar]], Harvard (BWH SPL)

== NA-MIC Collaborators ==
These NA-MIC collaborators are funded under the "Collaboration with NCBC" PAR.
#Nicole Grosland, UIowa
#Vincent Magnotta, UIowa
#Steve Pieper, Isomics
#James Daunais, Wake Forest
#Robert Kraft, Wake Forest
#Chris Wyatt, Virginia Tech
#Kilian Pohl, Harvard (BWH SPL)
#Sandy Wells, Harvard (BWH SPL)
#Kevin Cleary, Georgetown
#Enrique Campos-Nanez, George Washington U.
#Patrick (Peng) Cheng, Georgetown
#Ziv Yaniv, Georgetown
#Nobuhiko Hata, Harvard (BWH)
#Curtis Lisle, KnowledgeVis

==NA-MIC EAB==

Our External Advisory Board members are listed [[EAB|here]].

== NA-MIC alumni ==
*[[User:Naucoin|Nicole Aucoin]], BWH, SPL
*Wendy Plesniak, Harvard, BWH, SPL
* [http://marchingcubes.org Bill Lorensen]
* [[User:Lzollei|Lilla Zollei]], MIT (CSAIL)
* Lauren O'Donnell, MIT (CSAIL)
* [http://people.csail.mit.edu/wanmei/ Wanmei Ou], MIT (CSAIL)
* [[Mahnaz_Maddah|Mahnaz Maddah]], MIT (CSAIL)
* Ramsey Al-Hakim, Georgia Tech
* [[User:Melonakos|John Melonakos]], Georgia Tech
* [[User:Lankton|Shawn Lankton]], Georgia Tech
* [[User:Nain|Delphine Nain]], Georgia Tech
* Xavier Le Faucheur, Georgia Tech
* [[User:Mohan|Vandana Mohan]], Georgia Tech
* Tom Fletcher, Utah
* [http://www.sci.utah.edu/cgi-bin/SCIpersonnel.pl?username=tolga Tolga Tasdizen], Utah
* [http://www.cs.utah.edu/~sbasu/ Saurav Basu], Utah
* Josh Snyder, Harvard (MGH)
* [[User:DavidTuch|David Tuch]], Harvard (MGH)
* [[User:Karthik|Karthik Krishnan]],Kitware
* [http://www.kitware.com/profile/team/cedilnik.html/ Andy Cedilnik], Kitware
* [[User:Mathieu|Mathieu Malaterre]], Kitware
* [http://www.stat.ucla.edu/~dinov/ Ivo Dinov], UCLA
* [[User:MichaelPan|Michael Pan]], UCLA
* Brendan Flaherty, UCSD
* [[User:Adamc|Adam Cohen]], Harvard (BWH PNL)
* [[User:Markd|Mark Dreusicke]], Harvard (BWH PNL)
* Martha Shenton, Harvard (BWH PNL) PI
* [http://lmi.bwh.harvard.edu/~sylvain/ Sylvain Bouix], Harvard (BWH PNL)
* [http://lmi.bwh.harvard.edu/~marc/ Marc Niethammer], Harvard (BWH PNL)
* [http://synapse.hitchcock.org/bios/saykin.shtml Andy Saykin], Dartmouth PI
* Bob Roth, Dartmouth
* [http://synapse.hitchcock.org/bios/flashman.shtml Laura Flashman], Dartmouth
* [http://www.dhmc.org/providers/dhmc_provider_634.html Thomas McAllister], Dartmouth
* Alan Green, Dartmouth
* John West, Dartmouth
* [http://synapse.hitchcock.org/bios/mchugh.shtml/ Tara McHugh], Dartmouth
* [http://synapse.hitchcock.org/bios/pixley.shtml Heather Pixley], Dartmouth
* Stephen Guerin, Dartmouth
* John MacDonald, Dartmouth
* [http://www.bic.uci.edu/faculty/sgpotkin.htm/ Steve Potkin], UCI PI
* [[User:Jfallon|James Fallon]], UCI
* Jessica Turner, UCI
* Martina Panzenboeck, UCI
* David Medina, UCI
* [http://www.ics.uci.edu/~smyth/ Padhraic Smyth], UCI
* [http://www.ics.uci.edu/~sternh/ Hal Stern], UCI
* Diane Highum, UCI
* [http://www.ess.uci.edu/~yu/ Yi Jin], UCI
* Liv Trondsen, UCI
* Fabio Macciardi, Toronto
* [http://www.utpsychiatry.ca/dirsearch.asp?id=130 Jim Kennedy], Toronto
* Aristotle Voineskos, Toronto
* [http://kotaro.naist.jp/~meg/eindex.html Megumi Nakao], NAIST

== "Friends and Family" ==

=== NIH ===

* [[User:Lysterp|Peter M. Lyster]]

=== mBIRN ===

* [[User:Akolasny|Anthony Kolasny]]
* [[User:Dmarcus|Dan Marcus]]
* [[User:Kikinis|Ron Kikinis]]

=== fBIRN ===

* [[User:Kikinis|Ron Kikinis]]

=== IGT ===

* [[User:Ibanez|Luis Ibanez]]
* [[User:Noby| Nobuhiko Hata]]

=== Other ===

2013 Summer Project Week:Sobolev Segmenter

2013-05-26T18:03:14Z

Anry:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:Sobolev_Segmenter2D.png|The 2D segmentation of tumor by Sobolev active contour.
Image:Sobolev_Segmenter3D.png|The 3D segmentation of tumor by Sobolev active contour.
</gallery>

==Key Investigators==
* UAB: Arie Nakhmani, LiangJia Zhu, Allen Tannenbaum
* BWH: Yi Gao, Ron Kikinis
* Utah: Rob MacLeod, Josh Cates

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
General segmentation tools can be used in a wide range of biomedical applications from tumor delineation to segmentation of the atrial wall used in the research on the atrial fibrillation problem.
Sobolev active contour is a [http://www.na-mic.org/Wiki/index.php/Projects:SobolevTracker|smooth general 2D segmenter] that can be used in the applications above. It can be extended for medical volume segmentation. Our objective is to implement Slicer's extension based on Sobolev active contours algorithm for volume segmentation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
We prepare C++ implementation of Sobolev active contour algorithm, and convert it to Slicer Commandline extension.
This version needs a few sparse initial contours on some slices of the segmented volume.

In another approach, we implement similar algorithm in Python, as an Editor effect of Slicer. In this case, only the definition of 3D depth, and a single click inside the area of the segmented target are needed for the algorithm to work.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Alpha versions of both approaches (C++ Commandline and Python Editor effect) have been prepared.

</div>
</div>

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a loadable Commandline extension and an Editor effect.

==References==
* A. Nakhmani, A. Tannenbaum, "Tracking with Adaptive Sobolev Snakes." Submitted to IEEE Transactions on Image Processing.

* A. Nakhmani, A. Tannenbaum, "Self-Crossing Detection and Location for Parametric Active Contours," IEEE Transactions on Image Processing, DOI:10.1109/TIP.2012.2188808, Volume 21, Issue 7, pp. 3150-3156, July 2012.

2013 Summer Project Week:Sobolev Segmenter

2013-05-26T18:02:42Z

Anry:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:Sobolev_Segmenter2D.png|The 2D segmentation of tumor by Sobolev active contour.
Image:Sobolev_Segmenter3D.png|The 3D segmentation of tumor by Sobolev active contour.
</gallery>

==Key Investigators==
* UAB: Arie Nakhmani, LiangJia Zhu, Allen Tannenbaum
* BWH: Yi Gao, Ron Kikinis
* Utah: Rob MacLeod, Josh Cates

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
General segmentation tools can be used in a wide range of biomedical applications from tumor delineation to segmentation of the atrial wall used in the research on the atrial fibrillation problem.
Sobolev active contour is a [http://www.na-mic.org/Wiki/index.php/Projects:SobolevTracker|smooth general 2D segmenter] that can be used in the applications above. It can be extended for medical volume segmentation. Our objective is to implement Slicer's extension based on Sobolev active contours algorithm for volume segmentation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
We prepare C++ implementation of Sobolev active contour algorithm, and convert it to Slicer Commandline extension.
This version needs a few sparse initial contours on some slices of the segmented volume.

In another approach, we implement similar algorithm in Python, as an Editor effect of Slicer. In this case, only the definition of 3D depth, and a single click inside the area of the segmented target are needed for the algorithm to work.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Alpha versions of both approaches (C++ Commandline and Python Editor effect) have been prepared.

</div>
</div>

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a loadable Commandline extension and an Editor effect.

==References==
* A. Nakhmani, A. Tannenbaum, "Tracking with Adaptive Sobolev Snakes." Submitted to
IEEE Transactions on Image Processing.

* A. Nakhmani, A. Tannenbaum, "Self-Crossing Detection and Location for Parametric
Active Contours," IEEE Transactions on Image Processing, DOI:10.1109/TIP.2012.2188808,
Volume 21, Issue 7, pp. 3150-3156, July 2012.

2013 Summer Project Week:Sobolev Segmenter

2013-05-26T17:59:23Z

Anry:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:Sobolev_Segmenter2D.png|The 2D segmentation of tumor by Sobolev active contour.
Image:Sobolev_Segmenter3D.png|The 3D segmentation of tumor by Sobolev active contour.
</gallery>

==Key Investigators==
* UAB: Arie Nakhmani, LiangJia Zhu, Allen Tannenbaum
* BWH: Yi Gao, Ron Kikinis
* Utah: Rob MacLeod, Josh Cates

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
General segmentation tools can be used in a wide range of biomedical applications from tumor delineation to segmentation of the atrial wall used in the research on the atrial fibrillation problem.
Sobolev active contour is a smooth general 2D segmenter that can be used in the applications above. It can be extended for medical volume segmentation. Our objective is to implement Slicer's extension based on Sobolev active contours algorithm for volume segmentation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
We prepare C++ implementation of Sobolev active contour algorithm, and convert it to Slicer Commandline extension.
This version needs a few sparse initial contours on some slices of the segmented volume.

In another approach, we implement similar algorithm in Python, as an Editor effect of Slicer. In this case, only the definition of 3D depth, and a single click inside the area of the segmented target are needed for the algorithm to work.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Alpha versions of both approaches (C++ Commandline and Python Editor effect) have been prepared.

</div>
</div>

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a loadable Commandline extension and an Editor effect.

==References==
* A. Nakhmani, A. Tannenbaum, "Tracking with Adaptive Sobolev Snakes." Submitted to
IEEE Transactions on Image Processing.

* A. Nakhmani, A. Tannenbaum, "Self-Crossing Detection and Location for Parametric
Active Contours," IEEE Transactions on Image Processing, DOI:10.1109/TIP.2012.2188808,
Volume 21, Issue 7, pp. 3150-3156, July 2012.

File:Sobolev Segmenter3D.png

2013-05-26T17:41:10Z

Anry: The 3D segmentation by Sobolev active contour

The 3D segmentation by Sobolev active contour

File:Sobolev Segmenter2D.png

2013-05-26T17:39:02Z

Anry: The 2D segmentation by Sobolev active contour

The 2D segmentation by Sobolev active contour

2013 Summer Project Week:Sobolev Segmenter

2013-05-26T17:35:54Z

Anry: Created page with '__NOTOC__ <gallery> Image:PW-MIT2013.png|Projects List Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus…'

Project Week/Template

2013-05-26T17:32:24Z

Anry: Undo revision 81173 by Anry (Talk)

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.
Image:genuFA.jpg|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.
</gallery>

==Instructions for Use of this Template==
#Please create a new wiki page with an appropriate title for your project using the convention 2013_Summer_Project_Week:<Project Name>
#Copy the entire text of this page into the page created above
#Link the created page into the list of projects for the project event
#Delete this section from the created page
#Send an email to tkapur at bwh.harvard.edu if you are stuck

==Key Investigators==
* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
* Utah: Tom Fletcher, Ross Whitaker

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are developing methods for analyzing diffusion tensor data along fiber tracts. The goal is to be able to make statistical group comparisons with fiber tracts as a common reference frame for comparison.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

Our approach for analyzing diffusion tensors is summarized in the IPMI 2007 reference below. The main challenge to this approach is <foo>.

Our plan for the project week is to first try out <bar>,...

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Software for the fiber tracking and statistical analysis along the tracts has been implemented. The statistical methods for diffusion tensors are implemented as ITK code as part of the [[NA-MIC/Projects/Diffusion_Image_Analysis/DTI_Software_and_Algorithm_Infrastructure|DTI Software Infrastructure]] project. The methods have been validated on a repeated scan of a healthy individual. This work has been published as a conference paper (MICCAI 2005) and a journal version (MEDIA 2006). Our recent IPMI 2007 paper includes a nonparametric regression method for analyzing data along a fiber tract.

</div>
</div>

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)

#ITK Module
#Slicer Module
##Built-in
##Extension -- commandline
##Extension -- loadable
#Other (Please specify)

==References==
*Fletcher P, Tao R, Jeong W, Whitaker R. [http://www.na-mic.org/publications/item/view/634 A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI.] Inf Process Med Imaging. 2007;20:346-358. PMID: 17633712.
* Corouge I, Fletcher P, Joshi S, Gouttard S, Gerig G. [http://www.na-mic.org/publications/item/view/292 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Med Image Anal. 2006 Oct;10(5):786-98. PMID: 16926104.
* Corouge I, Fletcher P, Joshi S, Gilmore J, Gerig G. [http://www.na-mic.org/publications/item/view/1122 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):131-9. PMID: 16685838.
* Goodlett C, Corouge I, Jomier M, Gerig G, A Quantitative DTI Fiber Tract Analysis Suite, The Insight Journal, vol. ISC/NAMIC/ MICCAI Workshop on Open-Source Software, 2005, Online publication: http://hdl.handle.net/1926/39 .

Project Week/Template

2013-05-26T17:32:02Z

Anry: Undo revision 81174 by Anry (Talk)

2013 Summer Project Week

2013-05-25T17:35:16Z

Anry: /* Atrial Fibrillation */

Back to [[Events]]
[[image:PW-MIT2013.png|300px]]

Dates: June 17-21, 2013.

Location: MIT, Cambridge, MA.

==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, June 17
!style="width:18%" |Tuesday, June 18
!style="width:18%" |Wednesday, June 19
!style="width:18%" |Thursday, June 20
!style="width:18%" |Friday, June 21
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"|'''NA-MIC Update Day'''
|
|bgcolor="#88aaae"|'''IGT and RT Day'''
|bgcolor="#faedb6"|'''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9am-12pm'''
|
|'''10-11am''' [[2013 Project Week Breakout Session:Slicer4Python|Slicer4 Python Modules, Testing, Q&A]] <br>
[[MIT_Project_Week_Rooms|Grier Room (Left)]]
|'''9:30-11pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2013 Project Week Breakout Session: SimpleITK|Slicer and SimpleITK]] (Hans)
[[MIT_Project_Week_Rooms#32-D507|32-D507]]
|'''10am-12pm: <font color="#4020ff">Breakout Session:'''</font><br>[[2013 Project Week Breakout Session: IGT|Image-Guided Therapy]] (Tina)
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
|'''10am-12pm:''' [[#Projects|Project Progress Updates]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|-
|bgcolor="#ffffdd"|'''12pm-1pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1pm-5:30pm'''
|'''1-1:05pm: <font color="#503020">Ron Kikinis: Welcome</font>'''
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3:30-4:30pm''' [[2013 Summer Project Week Breakout Session:SlicerExtensions|Slicer4 Extensions]] (Jean-Christophe Fillion-Robin) <br>
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Room (Left)]]
|'''1-3pm:''' [[Renewal-06-2013|NA-MIC Renewal]] <br>PIs <br>Closed Door Session with Ron
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
<br>----------------------------------------<br>
'''3-4pm:''' [[2013_Tutorial_Contest|Tutorial Contest Presentations]] <br>
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|'''12:45-1pm:''' [[Events:TutorialContestJune2013|Tutorial Contest Winner Announcement]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|'''3-5:30pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2013 Summer Project Week Breakout Session:RT|Radiation Therapy]] (Greg, Csaba)
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
|
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|
|}

== '''Projects''' ==

Please use [http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template this template] to create wiki pages for your project. Then link the page here with a list of key personnel.

===Huntington's Disease===
* [[Dynamically Configurable Quality Assurance Module for Large Huntington's Disease Database Frontend]] (Dave)
* [[DWIConvert]] (Kent)
* [[Learn and Apply FiberBundleLabelSelect for Huntington's Disease Data]] (Hans, Demian)
* [[Investigate Potential Tensor Computation Improvement via Positive Semi-Definite (PSD) Tensor Estimation]] (Hans)
* [[Enhance and update SPL atlas]] (Dave, Hans)

===Traumatic Brain Injury===
* Validation and testing of 3D Slicer modules implementing the Utah segmentation algorithm for traumatic brain injury (Andrei Irimia, Micah Chambers, Bo Wang, Marcel Prastawa, Guido Gerig, Jack van Horn)
* Visualization and quantification of peri-contusional white matter bundles in traumatic brain injury using diffusion tensor imaging (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)
* Clinically oriented assessment of local changes in the properties of white matter affected by intra-cranial hemorrhage (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)
* Investigation of the peri-lesional penumbra in traumatic brain injury using diffusion tensor imaging to isolate longitudinal changes in white matter integrity (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)
* Reconstruction and visualization of the corticospinal tract in traumatic brain injury in the presence of severe hematoma and CSF-perfused edematous tissue using diffusion tensor imaging (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)

===Atrial Fibrillation===
* [[2013_Summer_Project_Week:CARMA_workflow_wizard|CARMA LA segmentation and enhancement quantification workflow wizard (Salma Bengali, Alan Morris, Brian Zenger, Josh Cates, Rob MacLeod)]]
* [[2013_Summer_Project_Week:CARMA_Documentataion|Cardiac MRI Toolkit Documentation Project (Salma Bengali, Alan Morris, Brian Zenger, Josh Cates, Rob MacLeod)]]
* [[2013_Summer_Project_Week:CARMA_Visualization|LA model visualization (Salma Bengali, Alan Morris, Josh Cates, Rob MacLeod)]]
* [[2013_Summer_Project_Week:CARMA_AutoLASeg|Automatic LA Segmentation with Graph Cuts Module (Salma Bengali, Alan Morris, Josh Cates, Gopal, Ross Whitaker, Rob MacLeod)]]
* [[2013_Summer_Project_Week:Sobolev_Segmenter|Medical Volume Segmentation Using Sobolev Active Contours (Arie Nakhmani, Yi Gao, LiangJia Zhu, Rob MacLeod, Josh Cates, Ron Kikinis, Allen Tannenbaum)]]

===Radiation Therapy===
* Landmark Registration (Steve, Nadya, Greg, Paolo, Erol)
* [[Slicer RT: DICOM-RT Export (Greg Sharp, Kevin Wang, Csaba Pinter)]]

===Device Integration with Slicer===
* Open-source electromagnetic trackers using OpenIGTLink (Peter Traneus Anderson, Tina Kapur, Sonia Pujol)

===IGT===
* [[2013_Summer_Project_Week:SlicerIGT_Extension| SlicerIGT extension]] (Tamas, Junichi, Laurent)
* Ultrasound Calibration (Matthew Toews, William Wells, Steven Aylward, Tamas Ungi)
* Application of Statistical Shape Modeling to Robot Assisted Spine Surgery (Marine Clogenson)
* [[2013_Summer_Project_Week:Epilepsy_Surgery|Identification of MRI Blurring in Temporal Lobe Epilepsy Surgery]] (Luiz Murta)
* Is Neurosurgical Rigid Registration really rigid? (Athena)
* [[2013_Summer_Project_Week:Liver_Trajectory_Management| Liver Trajectory Management]] (Laurent, Junichi)
* [[2013_Summer_Project_Week:4DUltrasound| 4D Ultrasound]] (Laurent, Junichi)

=== '''Informatics'''===
* [[2013_Summer_Project_Week:Biomedical_Image_Computing_Teaching_Modules|3D Slicer based Biomedical image computing teaching modules (A.Vilchis, J-C. Avila-Vilchis, S.Pujol)]]
* [[2013_Summer_Project_Week:Robot_Control| Robot Control (A.Vilchis, J-C. Avila-Vilchis, S.Pujol)]]

==='''Infrastructure'''===
* [[2013_Summer_Project_Week:MarkupsModuleSummer2013| Markups/Annotations rewrite]] (Nicole Aucoin)
* Brain atlas optimisations demo (Marianna)
* Provenance
* Patient hierarchies (Csaba Pinter)
* Sample data (Steve Pieper, Jim Miller)
** content addressable data, in external data processing in Slicer, cmake file for external data, when write test can decorate the data file name with macro keywords saying it's external
* Plastimatch in NiPype (Paolo, Dave, Hans)
** look for commonalities/reuse of CompareVolumes
* iPython in Slicer (Hans, Jc, Dave)
* Optimizing start time of slicer (Jc)

== '''Background''' ==

We are pleased to announce the 17th PROJECT WEEK of hands-on research and development activity for applications in Neuroscience, Image-Guided Therapy and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. If you would like to learn more about this event, please [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week click here to join our mailing list].

Active preparation begins on Thursday, April 25th at 3pm ET, with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 40-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.

This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], [http://www.cimit.org CIMIT], and OCAIRO. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== '''Logistics''' ==

*'''Dates:''' June 17-21, 2013.
*'''Location:''' MIT.
*'''REGISTRATION:''' http://www.regonline.com/namic2013summerprojweek. Please note that as you proceed to the checkout portion of the registration process, RegOnline will offer you a chance to opt into a free trial of ACTIVEAdvantage -- click on "No thanks" in order to finish your Project Week registration.
*'''Registration Fee:''' $300.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Room sharing''': If interested, add your name to the list before May 27th. See [[2013_Summer_Project_Week/RoomSharing|here]]

== '''Preparation''' ==

# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list
# The NA-MIC engineering team will be discussing projects in a their [http://wiki.na-mic.org/Wiki/index.php/Engineering:TCON_2013 weekly teleconferences]. Participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!
# By 3pm ET on Thursday May 8, all participants to add a one line title of their project to #Projects
# By 3pm ET on Thursday June 6, all project leads to complete [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.
# By 3pm on June 13: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Matt)
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. XNAT/MIDAS). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Where possible, setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Matt)
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...
# People doing Slicer related projects should come to project week with slicer built on your laptop.
## See the [http://www.slicer.org/slicerWiki/index.php/Documentation/4.0/Developers Developer Section of slicer.org] for information.
## Projects to develop extension modules should be built against the latest Slicer4 trunk.

== '''Registrants''' ==

Do not add your name to this list - it is maintained by the organizers based on your paid registration. ([http://www.regonline.com/Register/Checkin.aspx?EventID=1233699 Please click here to register.])

#Peter Anderson, retired, traneus@verizon.net
#Nicole Aucoin, BWH, nicole@bwh.harvard.edu
#Francois Budin, NIRAL-UNC, fbudin@unc.edu
#Micah Chambers, UCLA, micahcc@ucla.edu
#Marine Clogenson, Ecole Polytechnique Federale de Lausanne (Switzerland), marine.clogenson@epfl.ch
#Andriy Fedorov, BWH, fedorov@bwh.harvard.edu
#Jean-Christophe Fillion-Robin, Kitware, jchris.fillionr@kitware.com
#Karl Fritscher, MGH, kfritscher@gmail.com
#Yi Gao, Univ AL Birmingham, gaoyi.cn@gmail.com
#Hans Johnson, Univ Iowa, hans-johnson@uiowa.edu
#Tina Kapur, BWH/HMS, tkapur@bwh.harvard.edu
#Ron Kikinis, HMS, kikinis@bwh.harvard.edu
#Rui Li, GE Research, li.rui@ge.com
#William Lorensen, Bill's Basement, bill.lorensen@gmail.com
#Sidong Liu, Univ Sydney (Australia), sliu7418@uni.sydney.edu.au
#Bradley Lowekamp, Medical Science & Computing Inc, bradley.lowekamp@nih.gov
#Jim Miller, GE Research, millerjv@ge.com
#Luis Murta, Univ Sao Paulo (Brazil), lomurta@gmail.com
#Arie Nakhmani, Univ AL Birmingham, anry@uab.edu
#Steve Pieper, Isomics Inc, pieper@isomics.com
#Csaba Pinter, Queen's Univ (Canada), pinter@cs.queensu.ca
#Sonia Pujol, HMS, spujol@bwh.harvard.edu
#Raul San Jose, BWH, rjosest@bwh.harvard.edu
#Nadya Shusharina, MGH, nshusharina@partners.org
#Matthew Toews, BWH/HMS, mt@bwh.harvard.edu
#David Welch, Univ Iowa, david-welch@uiowa.edu
#Phillip White, BWH/HMS, white@bwh.harvard.edu
#Paolo Zaffino, University Magna Graecia of Catanzaro (Italy), p.zaffino@unicz.it

Project Week/Template

2013-05-25T17:23:26Z

Anry:

Project Week/Template

2013-05-25T17:21:39Z

Anry:

2013 Summer Project Week

2013-05-25T17:07:15Z

Anry: /* Atrial Fibrillation */

Back to [[Events]]
[[image:PW-MIT2013.png|300px]]

Dates: June 17-21, 2013.

Location: MIT, Cambridge, MA.

==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, June 17
!style="width:18%" |Tuesday, June 18
!style="width:18%" |Wednesday, June 19
!style="width:18%" |Thursday, June 20
!style="width:18%" |Friday, June 21
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"|'''NA-MIC Update Day'''
|
|bgcolor="#88aaae"|'''IGT and RT Day'''
|bgcolor="#faedb6"|'''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9am-12pm'''
|
|'''10-11am''' [[2013 Project Week Breakout Session:Slicer4Python|Slicer4 Python Modules, Testing, Q&A]] <br>
[[MIT_Project_Week_Rooms|Grier Room (Left)]]
|'''9:30-11pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2013 Project Week Breakout Session: SimpleITK|Slicer and SimpleITK]] (Hans)
[[MIT_Project_Week_Rooms#32-D507|32-D507]]
|'''10am-12pm: <font color="#4020ff">Breakout Session:'''</font><br>[[2013 Project Week Breakout Session: IGT|Image-Guided Therapy]] (Tina)
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
|'''10am-12pm:''' [[#Projects|Project Progress Updates]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|-
|bgcolor="#ffffdd"|'''12pm-1pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1pm-5:30pm'''
|'''1-1:05pm: <font color="#503020">Ron Kikinis: Welcome</font>'''
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3:30-4:30pm''' [[2013 Summer Project Week Breakout Session:SlicerExtensions|Slicer4 Extensions]] (Jean-Christophe Fillion-Robin) <br>
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Room (Left)]]
|'''1-3pm:''' [[Renewal-06-2013|NA-MIC Renewal]] <br>PIs <br>Closed Door Session with Ron
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
<br>----------------------------------------<br>
'''3-4pm:''' [[2013_Tutorial_Contest|Tutorial Contest Presentations]] <br>
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|'''12:45-1pm:''' [[Events:TutorialContestJune2013|Tutorial Contest Winner Announcement]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|'''3-5:30pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2013 Summer Project Week Breakout Session:RT|Radiation Therapy]] (Greg, Csaba)
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
|
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|
|}

== '''Projects''' ==

Please use [http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template this template] to create wiki pages for your project. Then link the page here with a list of key personnel.

===Huntington's Disease===
* [[Dynamically Configurable Quality Assurance Module for Large Huntington's Disease Database Frontend]] (Dave)
* [[DWIConvert]] (Kent)
* [[Learn and Apply FiberBundleLabelSelect for Huntington's Disease Data]] (Hans, Demian)
* [[Investigate Potential Tensor Computation Improvement via Positive Semi-Definite (PSD) Tensor Estimation]] (Hans)
* [[Enhance and update SPL atlas]] (Dave, Hans)

===Traumatic Brain Injury===
* Validation and testing of 3D Slicer modules implementing the Utah segmentation algorithm for traumatic brain injury (Andrei Irimia, Micah Chambers, Bo Wang, Marcel Prastawa, Guido Gerig, Jack van Horn)
* Visualization and quantification of peri-contusional white matter bundles in traumatic brain injury using diffusion tensor imaging (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)
* Clinically oriented assessment of local changes in the properties of white matter affected by intra-cranial hemorrhage (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)
* Investigation of the peri-lesional penumbra in traumatic brain injury using diffusion tensor imaging to isolate longitudinal changes in white matter integrity (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)
* Reconstruction and visualization of the corticospinal tract in traumatic brain injury in the presence of severe hematoma and CSF-perfused edematous tissue using diffusion tensor imaging (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)

===Atrial Fibrillation===
* [[2013_Summer_Project_Week:CARMA_workflow_wizard|CARMA LA segmentation and enhancement quantification workflow wizard (Salma Bengali, Alan Morris, Brian Zenger, Josh Cates, Rob MacLeod)]]
* [[2013_Summer_Project_Week:CARMA_Documentataion|Cardiac MRI Toolkit Documentation Project (Salma Bengali, Alan Morris, Brian Zenger, Josh Cates, Rob MacLeod)]]
* [[2013_Summer_Project_Week:CARMA_Visualization|LA model visualization (Salma Bengali, Alan Morris, Josh Cates, Rob MacLeod)]]
* [[2013_Summer_Project_Week:CARMA_AutoLASeg|Automatic LA Segmentation with Graph Cuts Module (Salma Bengali, Alan Morris, Josh Cates, Gopal, Ross Whitaker, Rob MacLeod)]]
* [[2013_Summer_Project_Week:Sobolev_Segmenter|Medical Volume Segmentation Using Sobolev Active Contours (Arie Nakhmani, Yi Gao, LiangJia Zhu, Rob MacLeod, Josh Cates, Ron Kikinis, and Allen Tannenbaum)]]

===Radiation Therapy===
* Landmark Registration (Steve, Nadya, Greg, Paolo, Erol)
* [[Slicer RT: DICOM-RT Export (Greg Sharp, Kevin Wang, Csaba Pinter)]]

===Device Integration with Slicer===
* Open-source electromagnetic trackers using OpenIGTLink (Peter Traneus Anderson, Tina Kapur, Sonia Pujol)

===IGT===
* [[2013_Summer_Project_Week:SlicerIGT_Extension| SlicerIGT extension]] (Tamas, Junichi, Laurent)
* Ultrasound Calibration (Matthew Toews, William Wells, Steven Aylward, Tamas Ungi)
* Application of Statistical Shape Modeling to Robot Assisted Spine Surgery (Marine Clogenson)
* [[2013_Summer_Project_Week:Epilepsy_Surgery|Identification of MRI Blurring in Temporal Lobe Epilepsy Surgery]] (Luiz Murta)
* Is Neurosurgical Rigid Registration really rigid? (Athena)
* [[2013_Summer_Project_Week:Liver_Trajectory_Management| Liver Trajectory Management]] (Laurent, Junichi)
* [[2013_Summer_Project_Week:4DUltrasound| 4D Ultrasound]] (Laurent, Junichi)

=== '''Informatics'''===
* [[2013_Summer_Project_Week:Biomedical_Image_Computing_Teaching_Modules|3D Slicer based Biomedical image computing teaching modules (A.Vilchis, J-C. Avila-Vilchis, S.Pujol)]]
* [[2013_Summer_Project_Week:Robot_Control| Robot Control (A.Vilchis, J-C. Avila-Vilchis, S.Pujol)]]

==='''Infrastructure'''===
* [[2013_Summer_Project_Week:MarkupsModuleSummer2013| Markups/Annotations rewrite]] (Nicole Aucoin)
* Brain atlas optimisations demo (Marianna)
* Provenance
* Patient hierarchies (Csaba Pinter)
* Sample data (Steve Pieper, Jim Miller)
** content addressable data, in external data processing in Slicer, cmake file for external data, when write test can decorate the data file name with macro keywords saying it's external
* Plastimatch in NiPype (Paolo, Dave, Hans)
** look for commonalities/reuse of CompareVolumes
* iPython in Slicer (Hans, Jc, Dave)
* Optimizing start time of slicer (Jc)

== '''Background''' ==

We are pleased to announce the 17th PROJECT WEEK of hands-on research and development activity for applications in Neuroscience, Image-Guided Therapy and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. If you would like to learn more about this event, please [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week click here to join our mailing list].

Active preparation begins on Thursday, April 25th at 3pm ET, with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 40-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.

This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], [http://www.cimit.org CIMIT], and OCAIRO. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== '''Logistics''' ==

*'''Dates:''' June 17-21, 2013.
*'''Location:''' MIT.
*'''REGISTRATION:''' http://www.regonline.com/namic2013summerprojweek. Please note that as you proceed to the checkout portion of the registration process, RegOnline will offer you a chance to opt into a free trial of ACTIVEAdvantage -- click on "No thanks" in order to finish your Project Week registration.
*'''Registration Fee:''' $300.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Room sharing''': If interested, add your name to the list before May 27th. See [[2013_Summer_Project_Week/RoomSharing|here]]

== '''Preparation''' ==

# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list
# The NA-MIC engineering team will be discussing projects in a their [http://wiki.na-mic.org/Wiki/index.php/Engineering:TCON_2013 weekly teleconferences]. Participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!
# By 3pm ET on Thursday May 8, all participants to add a one line title of their project to #Projects
# By 3pm ET on Thursday June 6, all project leads to complete [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.
# By 3pm on June 13: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Matt)
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. XNAT/MIDAS). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Where possible, setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Matt)
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...
# People doing Slicer related projects should come to project week with slicer built on your laptop.
## See the [http://www.slicer.org/slicerWiki/index.php/Documentation/4.0/Developers Developer Section of slicer.org] for information.
## Projects to develop extension modules should be built against the latest Slicer4 trunk.

== '''Registrants''' ==

Do not add your name to this list - it is maintained by the organizers based on your paid registration. ([http://www.regonline.com/Register/Checkin.aspx?EventID=1233699 Please click here to register.])

#Peter Anderson, retired, traneus@verizon.net
#Nicole Aucoin, BWH, nicole@bwh.harvard.edu
#Francois Budin, NIRAL-UNC, fbudin@unc.edu
#Micah Chambers, UCLA, micahcc@ucla.edu
#Marine Clogenson, Ecole Polytechnique Federale de Lausanne (Switzerland), marine.clogenson@epfl.ch
#Andriy Fedorov, BWH, fedorov@bwh.harvard.edu
#Jean-Christophe Fillion-Robin, Kitware, jchris.fillionr@kitware.com
#Karl Fritscher, MGH, kfritscher@gmail.com
#Yi Gao, Univ AL Birmingham, gaoyi.cn@gmail.com
#Hans Johnson, Univ Iowa, hans-johnson@uiowa.edu
#Tina Kapur, BWH/HMS, tkapur@bwh.harvard.edu
#Ron Kikinis, HMS, kikinis@bwh.harvard.edu
#Rui Li, GE Research, li.rui@ge.com
#William Lorensen, Bill's Basement, bill.lorensen@gmail.com
#Sidong Liu, Univ Sydney (Australia), sliu7418@uni.sydney.edu.au
#Bradley Lowekamp, Medical Science & Computing Inc, bradley.lowekamp@nih.gov
#Jim Miller, GE Research, millerjv@ge.com
#Luis Murta, Univ Sao Paulo (Brazil), lomurta@gmail.com
#Arie Nakhmani, Univ AL Birmingham, anry@uab.edu
#Steve Pieper, Isomics Inc, pieper@isomics.com
#Csaba Pinter, Queen's Univ (Canada), pinter@cs.queensu.ca
#Sonia Pujol, HMS, spujol@bwh.harvard.edu
#Raul San Jose, BWH, rjosest@bwh.harvard.edu
#Nadya Shusharina, MGH, nshusharina@partners.org
#Matthew Toews, BWH/HMS, mt@bwh.harvard.edu
#David Welch, Univ Iowa, david-welch@uiowa.edu
#Phillip White, BWH/HMS, white@bwh.harvard.edu
#Paolo Zaffino, University Magna Graecia of Catanzaro (Italy), p.zaffino@unicz.it

Algorithm:BU

2012-11-21T23:43:58Z

Anry: /* Multiple Interacting Objects Tracking With Adaptive Sobolev Active Contours */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =

At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Boston University/UAB Projects =

{| cellpadding="10" style="text-align:left;"

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== [[Projects:SobolevTracker|Object Tracking With Adaptive Sobolev Active Contours]] ==

In this project we propose adaptive tracking mechanism which can be used in military and civilian surveillance applications as well as in medical video applications, or 3D volume segmentation. The proposed Sobolev active contour model overcomes the
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and
low contrast objects. Experimental results show the advantages of our approach compared
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]

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| | [[Image:LiverFibrosisHist.png|200px|]]
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== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==

This project is conducted in collaboration with the Boston Medical Center. We are trying
to provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data
has much lower resolution than histological image data, but it includes the entire liver
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)
in diﬀusion MRI correlates with the ﬁbrosis stage. [[Projects:LiverFibrosisStaging|More...]]

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| | [[Image:Heart_topology.jpg|200px|]]
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== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]

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| | [[Image:toT1e1.png|200px|]]
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==

Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension
to the principal axis transformation method for ﬁnding robust rigid transformation of two
volumes. The additional elastic registration is based on a volume registration method
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]

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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==

We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured. [[Projects:MultiScaleShapeSegmentation|More...]]

<font color="red">'''New: '''</font> Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.

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| | [[Image:3D_Segmentation_LA.png|200px]]
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

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| | [[Image:Pain1.JPG|200px]]
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

<font color="red">'''New: '''</font> Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.

|-

| | [[Image:MultiObjSeg.png|200px|]]
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.

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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.

<font color="red">'''New: '''</font> Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .

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| | [[File:LASegAxialView.png|200px]]
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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==

The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), in submission.

<font color="red">'''New: '''</font>L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

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| | [[Image:ScarSeg_EM.png|200px]]
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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]

<font color="red">'''New: '''</font> Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.

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| | [[Image:LongitudinalAFib.png|200px]]
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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]

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| | [[Image:LRV_Wall.png|200px]]
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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.

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| | [[Image:RiskMassSeg.png|200px]]
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.

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| | [[Image:GT-DWI-Reorientation-1.jpg|300px]]
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

<font color="red">'''New: '''</font> Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation. Neuroimage, volume 45, 2009, pp. 123-132.

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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.

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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)

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| | [[Image:KVoutSegTightMod.png|200px]]
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==

An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]

<font color="red">'''New: '''</font> I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.

<font color="red">'''New: '''</font> P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.

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| | [[Image:PostRegFleshSkeleton.png|200px]]
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==

A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]

<font color="red">'''New: '''</font> I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

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| | [[Image:Model3D_upTrans.png|200px]]
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.

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| | [[Image:Circle seg.PNG|200px|]]
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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115.

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| | [[Image:ZoomedResultWithModel.png|200px]]
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.

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| | [[Image:P1_small.png|200px|]]
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

<font color="red">'''New: '''</font> X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.

<font color="red">'''New: '''</font> Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.

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| | [[Image:Results brain sag.JPG|200px]]
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==

The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.

<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.

<font color="red">'''New: '''</font> Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

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| | [[Image:GT-SPD-img1.png|200px|]]
| |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

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| | [[Image:Dlpfc1.jpg|200px|]]
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

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| | [[Image:Striatum1.png|200px|]]
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

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| | [[Image:Fig1yan.PNG|200px|]]
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

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| | [[Image:Stochastic-snake.png|200px|]]
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

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| | [[Image:GT-SulciOutlining1.jpg|200px]]
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

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| | [[Image:Table1.png|200px|]]
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

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| | [[Image:Gatech SlicerModel2.jpg|200px]]
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

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Projects:SobolevTracker

2012-11-21T23:43:27Z

Anry: /* Multiple Interacting Objects Tracking With Adaptive Sobolev Active Contours */

= Tracking With Adaptive Sobolev Active Contours =

In this project we propose adaptive tracking mechanism which can be used in military and civilian surveillance applications as well as in medical video applications, or 3D volume segmentation. The proposed Sobolev active contour model overcomes the problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with clutter and noise in the scenes, object deformations, partial and entire object occlusions, and low contrast objects. Experimental results show the advantages of our approach compared to state-of-the-art visual trackers [1].

In addition, we have proposed an algorithm for active contour self-crossing detection and elimination [2]. The problem of self-crossing is well known for parametric active contours, and it present a very important obstacle for the successful tracking. The proposed solution is based on topological properties of closed contours.

== Results ==

Figures 1-3 show an example of three frames of successful hand tracking, despite of different out of plane deformations, changing motion dynamics, and contour self crossings.

<gallery>
Image:HandTracking.png|Figure 1. Tracked frames of video sequence
Image:HandTracking2.png|Figure 2.
Image:HandTracking3.png|Figure 3.

</gallery>

= Publications =

1. A. Nakhmani, A. Tannenbaum, "Tracking with Adaptive Sobolev Snakes." Submitted to
IEEE Transactions on Image Processing.

2. A. Nakhmani, A. Tannenbaum, "Self-Crossing Detection and Location for Parametric
Active Contours," IEEE Transactions on Image Processing, DOI:10.1109/TIP.2012.2188808,
Volume 21, Issue 7, pp. 3150-3156, July 2012.

= Key Investigators =

* UAB: Arie Nakhmani and Allen Tannenbaum

File:HandTracking3.png

2012-11-21T23:20:46Z

Anry:

File:HandTracking2.png

2012-11-21T23:20:39Z

Anry:

Algorithm:BU

2012-11-21T19:31:33Z

Anry: /* Multiple Interacting Objects Tracking With Adaptive Sobolev Active Contours */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =

At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Boston University/UAB Projects =

{| cellpadding="10" style="text-align:left;"

| | [[Image:HandTracking.png|200px|]]
| |

== [[Projects:SobolevTracker|Multiple Interacting Objects Tracking With Adaptive Sobolev Active Contours]] ==

In this project we propose adaptive tracking mechanism which can be used in military and civilian surveillance applications as well as in medical video applications, or 3D volume segmentation. The proposed Sobolev active contour model overcomes the
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and
low contrast objects. Experimental results show the advantages of our approach compared
to state-of-the-art visual trackers.[[Projects:SobolevTracker|More...]]

|-

| | [[Image:LiverFibrosisHist.png|200px|]]
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== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==

This project is conducted in collaboration with the Boston Medical Center. We are trying
to provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data
has much lower resolution than histological image data, but it includes the entire liver
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)
in diﬀusion MRI correlates with the ﬁbrosis stage. [[Projects:LiverFibrosisStaging|More...]]

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| | [[Image:Heart_topology.jpg|200px|]]
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== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]

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| | [[Image:toT1e1.png|200px|]]
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==

Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension
to the principal axis transformation method for ﬁnding robust rigid transformation of two
volumes. The additional elastic registration is based on a volume registration method
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]

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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==

We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured. [[Projects:MultiScaleShapeSegmentation|More...]]

<font color="red">'''New: '''</font> Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.

|-

| | [[Image:3D_Segmentation_LA.png|200px]]
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

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| | [[Image:Pain1.JPG|200px]]
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

<font color="red">'''New: '''</font> Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.

|-

| | [[Image:MultiObjSeg.png|200px|]]
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.
|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.

<font color="red">'''New: '''</font> Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .

|-

| | [[File:LASegAxialView.png|200px]]
| |

== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==

The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), in submission.

<font color="red">'''New: '''</font>L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

|-

| | [[Image:ScarSeg_EM.png|200px]]
| |

== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]

<font color="red">'''New: '''</font> Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.

|-

| | [[Image:LongitudinalAFib.png|200px]]
| |

== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]

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| | [[Image:LRV_Wall.png|200px]]
| |

== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.

|-

| | [[Image:RiskMassSeg.png|200px]]
| |

== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|300px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

<font color="red">'''New: '''</font> Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation. Neuroimage, volume 45, 2009, pp. 123-132.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)

|-

| | [[Image:KVoutSegTightMod.png|200px]]
| |

== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==

An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]

<font color="red">'''New: '''</font> I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.

<font color="red">'''New: '''</font> P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.

|-

| | [[Image:PostRegFleshSkeleton.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==

A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]

<font color="red">'''New: '''</font> I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.

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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115.

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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.

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| | [[Image:P1_small.png|200px|]]
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.

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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

<font color="red">'''New: '''</font> X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.

<font color="red">'''New: '''</font> Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.

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| | [[Image:TruckInitialization.png|200px|]]
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.

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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==

The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.

<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.

<font color="red">'''New: '''</font> Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.

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| | [[Image:Gatech caudateBands.PNG|200px]]
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

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Projects:SobolevTracker

2012-11-21T19:30:24Z

Anry: Created page with '= Multiple Interacting Objects Tracking With Adaptive Sobolev Active Contours = In this project we propose adaptive tracking mechanism which can be used in military and civilian…'

= Multiple Interacting Objects Tracking With Adaptive Sobolev Active Contours =

In this project we propose adaptive tracking mechanism which can be used in military and civilian surveillance applications as well as in medical video applications, or 3D volume segmentation. The proposed Sobolev active contour model overcomes the problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with clutter and noise in the scenes, object deformations, partial and entire object occlusions, and low contrast objects. Experimental results show the advantages of our approach compared to state-of-the-art visual trackers.

Algorithm:BU

2012-11-21T19:29:25Z

Anry: /* Boston University/UAB Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =

At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Boston University/UAB Projects =

{| cellpadding="10" style="text-align:left;"

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== [[Projects:SobolevTracker|Multiple Interacting Objects Tracking With Adaptive Sobolev Active Contours]] ==

In this project we propose adaptive tracking mechanism which can be used in military and civilian surveillance applications as well as in medical video applications, or 3D volume segmentation. The proposed Sobolev active contour model overcomes the
problems of occlusions and changes in scale by adaptive tweaking of the rigidity parameters. The proposed tracking algorithms work in a variety of scenarios and deal naturally with
clutter and noise in the scenes, object deformations, partial and entire object occlusions, and
low contrast objects. Experimental results show the advantages of our approach compared
to state-of-the-art visual trackers.

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| | [[Image:LiverFibrosisHist.png|200px|]]
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== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==

This project is conducted in collaboration with the Boston Medical Center. We are trying
to provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data
has much lower resolution than histological image data, but it includes the entire liver
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)
in diﬀusion MRI correlates with the ﬁbrosis stage. [[Projects:LiverFibrosisStaging|More...]]

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== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]

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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==

Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension
to the principal axis transformation method for ﬁnding robust rigid transformation of two
volumes. The additional elastic registration is based on a volume registration method
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]

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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==

We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured. [[Projects:MultiScaleShapeSegmentation|More...]]

<font color="red">'''New: '''</font> Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.

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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

<font color="red">'''New: '''</font> Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.

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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.

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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.

<font color="red">'''New: '''</font> Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .

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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==

The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), in submission.

<font color="red">'''New: '''</font>L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]

<font color="red">'''New: '''</font> Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.

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| | [[Image:LongitudinalAFib.png|200px]]
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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]

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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.

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| | [[Image:RiskMassSeg.png|200px]]
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.

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| | [[Image:GT-DWI-Reorientation-1.jpg|300px]]
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

<font color="red">'''New: '''</font> Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation. Neuroimage, volume 45, 2009, pp. 123-132.

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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.

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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)

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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==

An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]

<font color="red">'''New: '''</font> I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.

<font color="red">'''New: '''</font> P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.

|-

| | [[Image:PostRegFleshSkeleton.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==

A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]

<font color="red">'''New: '''</font> I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

|-

| | [[Image:Model3D_upTrans.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115.

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

<font color="red">'''New: '''</font> X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.

<font color="red">'''New: '''</font> Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==

The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.

<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.

<font color="red">'''New: '''</font> Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:GT-SPD-img1.png|200px|]]
| |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

File:HandTracking.png

2012-11-21T19:11:38Z

Anry:

Projects:RegistrationTBI

2012-11-21T19:04:39Z

Anry: /* Future Work */

Back to [[Algorithm:BU|Boston University Algorithms]]
__NOTOC__
= Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units =

= Description =
An estimated 1.7 million Americans sustain traumatic brain injuries (TBI's) every year. The large number of recent TBI cases in soldiers returning from military conflicts has highlighted the critical need for improvement of TBI care and treatment, and has drawn sustained attention to the need for improved methodologies of TBI neuroimaging data analysis. Neuroimaging of TBI is vital for surgical planning by providing important information for anatomic localization and surgical navigation, as well as for monitoring patient case evolution over time. Approximately 2 days after the acute injury, magnetic resonance imaging (MRI) becomes preferable to computed tomography (CT) for the purpose of lesion characterization, and the use of various MR sequences tailored to capture distinct aspects of TBI pathology provides clinicians with essential complementary information for the assessment of TBI-related anatomical insults and pathophysiology.

Image registration plays an essential role in a wide variety of TBI data analysis workflows. It aims to find a transformation between two image sets such that the transformed image becomes similar to the target image according to some chosen metric or criterion. Typically, a similarity measure is first established to quantify how `close` two image volumes are to each other. Next, the transformation that maximizes this similarity is typically computed through an optimization process which constrains the transformation to a predetermined class, such as rigid, affine or deformable. Numerous challenges associated with the task of TBI volume co-registration can exist if data acquisition is performed multimodally, and additional complexities can also arise due to the large degree of algorithmic robustness that may be required in order to properly address pathology-related deformations. Many conventional methods use the sum of squared differences of intensity values between two image sets as a similarity measure, which can perform poorly or even fail for TBI volume registration. Consequently, because the deformation of patient anatomy and soft tissues cannot typically be represented by rigid transforms, image registration often requires deformable image registration (DIR), i.e., the necessity of applying nonparametric infinite-dimensional transformations.

This paper proposes to replace the Mutual Information (MI) criterion for registration with the Bhattachayya distance [1] within a multimodal DIR framework [3]. The advantage of BD over MI is the superior behavior of the square root function compared to that of the logarithm at zero, which yields a more stable algorithm.
This framework we describe takes into account the physical models of tissue motion to regularize the deformation fields and also involves free-form deformation. On the other hand, the DIR algorithm is computationally expensive when implemented on conventional central processing units, which can be detrimental particularly when three-dimensional (3D) volumes-rather than 2D images-need to be co-registered. In clinical settings that involve acute TBI care, the amount of time required by the processing of neuroimaging data sets from patients in critical condition should be minimized. To meet this clinical requirement, we have implemented our algorithm on a graphics processing unit (GPU) platform [2].

== Result ==

MR volumes were acquired at 3 T using a Siemens Trio TIM scanner (Siemens AG, Erlangen, Germany). Because assessing the time evolution of TBI between the acute to the chronic stage is of great interest in the clinical field in order to evaluate case evolution, scanning sessions were held both several days (acute baseline) as well as 6 months (chronic follow-up) after the traumatic injury event. To eliminate the effect of different scanner parameters during each scanning session, every subject was scanned using the same scanner for both acute and chronic time points. The MP-RAGE sequence (Mugler and Brookeman, 1990) was used to acquire T1-weighted images. In addition, MR data were also acquired using fluid-attenuated inversion recovery (FLAIR, (De Coene et al., 1992)), gradient-recalled echo (GRE) T2-weighted images as well as diffusion weighted imaging (DWI), and perfusion imaging.

Before applying our deformable registration algorithm, all image volumes were co-registered by rigid-body transformation to the pre-contrast T1-weighted volume acquired during the acute baseline scanning session. This helps to correct for head tilt and reduce error in computing the local deformation fields. Another technique that was employed before performing the registration is skull stripping, which was useful in our case because images acquired at the acute stage exhibit appreciably more extracranial swelling compared to images acquired chronically. Since all modalities are co-registered to T1, we only need to perform the skull stripping once, i.e. on the T1 volume. Skull stripping is necessary because, without it, the DIR algorithm would deform the interior of the brain to match the outside boundary. This type of deformation is mathematically valid, but does not yield anatomically plausible results. Two possible solutions to this problem are either adding prior knowledge on the boundary or applying skull stripping, of which we opt for the latter due to its common usage in image processing. We use the BrainSuit software [http://users.loni.ucla.edu/~shattuck/brainsuite/corticalsurface/bse/] for the skull stripping.

[[Image:ToT1e1.png|800px]]

Excluding preprocessing steps, the registration of two volumes of sizes 256x256x60 is found to require 6 seconds on the GPU. Registration results are illustrated for a 2D slice in the above figure for acute stage. The norm of the deformation fields and its 2D motion grid are also included. For T2, FLAIR and GRE volumes, the largest amount of deformation is observed bilaterally in the deep periventricular white matter, possibly as a consequence of hemorrhage and/or CSF infiltration into edemic regions, which can alter voxel intensities in GRE and FLAIR imaging, respectively. In the case of DWI, notable deformation is observed frontally and frontolaterally; in the former case, this may be the result of warping artifacts due to the large drop in the physical properties of tissues at the interfaces between brain, bone and air. In the latter case, the deformation is possibly due to the presence of TBI-related edema, which can substantially alter local diffusivity values. Similar effects due to these causes are observed with DWI and with perfusion imaging in both acute and chronic scans.

== Future Work ==

Future work will focus on registration of TBI volumes across time in terms of registering acute to chronic or vice versa. There are a large number of registration algorithms that assume the
smoothness of the vector flow, i.e., the deformation is
diffeomorphic. However, when registering TBI across time, the deformation is not well-defined, let alone
to be diffeomorphic, at some regions where bleeding or lesion
occurs. It is challenging and important to design a registration
algorithm that can deal with topological changes for TBI patients.
One possible approach is to use combination of locally rigid and non-rigid transforms based on visual features such as MIND descriptor [4]. Some regions of new lesions cannot be explained
by minor elastic registration, thus simultaneous registration and segmentation of lesions is
needed. We are working on the improved volume matching with boundary conditions based
on this segmentation.

= Key Investigators =

Georgia Tech: Yifei Lou and Patricio Vela

UAB: Arie Nakhmani and Allen Tannenbaum

UCLA: Andrei Irimia, Micah C. Chambers, Jack Van Horn and Paul M. Vespa

= References =
1.Yifei Lou, Andrei Irimia, Patricio Vela, Micah C. Chambers, Jack Van Horn, Paul M. Vespa and Allen Tannenbaum. Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes via the Bhattacharyya Distance. Submitted to IEEE Transactions on Bioengineering, 2012

2. Yifei Lou, Xun Jia, Xuejun Gu and Allen Tannenbaum. A GPU-based Implementation of Multimodal Deformable Image Registration Based on Mutual Information or Bhattacharyya Distance. Insight Journal, 2011. [http://www.midasjournal.org/browse/publication/803]

3. E. D’Agostino, F. Maes, D. Vandermeulen, and P. Suetens. A viscous fluid model for multimodal non-rigid image registration using mutual information,” MICCAI, 2002, pp. 541–548

4. M.P. Heinrich, M. Jenkinson, M. Bhushan, T. Matin, F. Gleeson, M. Brady, J.A. Schnabel. MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration. Medical Image Analysis. Vol. 16(7) Oct. 2012, pp. 1423–1435, Special Issue on MICCAI 2011

Projects:RegistrationTBI

2012-11-21T19:04:25Z

Anry: /* References */

Back to [[Algorithm:BU|Boston University Algorithms]]
__NOTOC__
= Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units =

= Description =
An estimated 1.7 million Americans sustain traumatic brain injuries (TBI's) every year. The large number of recent TBI cases in soldiers returning from military conflicts has highlighted the critical need for improvement of TBI care and treatment, and has drawn sustained attention to the need for improved methodologies of TBI neuroimaging data analysis. Neuroimaging of TBI is vital for surgical planning by providing important information for anatomic localization and surgical navigation, as well as for monitoring patient case evolution over time. Approximately 2 days after the acute injury, magnetic resonance imaging (MRI) becomes preferable to computed tomography (CT) for the purpose of lesion characterization, and the use of various MR sequences tailored to capture distinct aspects of TBI pathology provides clinicians with essential complementary information for the assessment of TBI-related anatomical insults and pathophysiology.

Image registration plays an essential role in a wide variety of TBI data analysis workflows. It aims to find a transformation between two image sets such that the transformed image becomes similar to the target image according to some chosen metric or criterion. Typically, a similarity measure is first established to quantify how `close` two image volumes are to each other. Next, the transformation that maximizes this similarity is typically computed through an optimization process which constrains the transformation to a predetermined class, such as rigid, affine or deformable. Numerous challenges associated with the task of TBI volume co-registration can exist if data acquisition is performed multimodally, and additional complexities can also arise due to the large degree of algorithmic robustness that may be required in order to properly address pathology-related deformations. Many conventional methods use the sum of squared differences of intensity values between two image sets as a similarity measure, which can perform poorly or even fail for TBI volume registration. Consequently, because the deformation of patient anatomy and soft tissues cannot typically be represented by rigid transforms, image registration often requires deformable image registration (DIR), i.e., the necessity of applying nonparametric infinite-dimensional transformations.

This paper proposes to replace the Mutual Information (MI) criterion for registration with the Bhattachayya distance [1] within a multimodal DIR framework [3]. The advantage of BD over MI is the superior behavior of the square root function compared to that of the logarithm at zero, which yields a more stable algorithm.
This framework we describe takes into account the physical models of tissue motion to regularize the deformation fields and also involves free-form deformation. On the other hand, the DIR algorithm is computationally expensive when implemented on conventional central processing units, which can be detrimental particularly when three-dimensional (3D) volumes-rather than 2D images-need to be co-registered. In clinical settings that involve acute TBI care, the amount of time required by the processing of neuroimaging data sets from patients in critical condition should be minimized. To meet this clinical requirement, we have implemented our algorithm on a graphics processing unit (GPU) platform [2].

== Result ==

MR volumes were acquired at 3 T using a Siemens Trio TIM scanner (Siemens AG, Erlangen, Germany). Because assessing the time evolution of TBI between the acute to the chronic stage is of great interest in the clinical field in order to evaluate case evolution, scanning sessions were held both several days (acute baseline) as well as 6 months (chronic follow-up) after the traumatic injury event. To eliminate the effect of different scanner parameters during each scanning session, every subject was scanned using the same scanner for both acute and chronic time points. The MP-RAGE sequence (Mugler and Brookeman, 1990) was used to acquire T1-weighted images. In addition, MR data were also acquired using fluid-attenuated inversion recovery (FLAIR, (De Coene et al., 1992)), gradient-recalled echo (GRE) T2-weighted images as well as diffusion weighted imaging (DWI), and perfusion imaging.

Before applying our deformable registration algorithm, all image volumes were co-registered by rigid-body transformation to the pre-contrast T1-weighted volume acquired during the acute baseline scanning session. This helps to correct for head tilt and reduce error in computing the local deformation fields. Another technique that was employed before performing the registration is skull stripping, which was useful in our case because images acquired at the acute stage exhibit appreciably more extracranial swelling compared to images acquired chronically. Since all modalities are co-registered to T1, we only need to perform the skull stripping once, i.e. on the T1 volume. Skull stripping is necessary because, without it, the DIR algorithm would deform the interior of the brain to match the outside boundary. This type of deformation is mathematically valid, but does not yield anatomically plausible results. Two possible solutions to this problem are either adding prior knowledge on the boundary or applying skull stripping, of which we opt for the latter due to its common usage in image processing. We use the BrainSuit software [http://users.loni.ucla.edu/~shattuck/brainsuite/corticalsurface/bse/] for the skull stripping.

[[Image:ToT1e1.png|800px]]

Excluding preprocessing steps, the registration of two volumes of sizes 256x256x60 is found to require 6 seconds on the GPU. Registration results are illustrated for a 2D slice in the above figure for acute stage. The norm of the deformation fields and its 2D motion grid are also included. For T2, FLAIR and GRE volumes, the largest amount of deformation is observed bilaterally in the deep periventricular white matter, possibly as a consequence of hemorrhage and/or CSF infiltration into edemic regions, which can alter voxel intensities in GRE and FLAIR imaging, respectively. In the case of DWI, notable deformation is observed frontally and frontolaterally; in the former case, this may be the result of warping artifacts due to the large drop in the physical properties of tissues at the interfaces between brain, bone and air. In the latter case, the deformation is possibly due to the presence of TBI-related edema, which can substantially alter local diffusivity values. Similar effects due to these causes are observed with DWI and with perfusion imaging in both acute and chronic scans.

== Future Work ==

Future work will focus on registration of TBI volumes across time in terms of registering acute to chronic or vice versa. There are a large number of registration algorithms that assume the
smoothness of the vector flow, i.e., the deformation is
diffeomorphic. However, when registering TBI across time, the deformation is not well-defined, let alone
to be diffeomorphic, at some regions where bleeding or lesion
occurs. It is challenging and important to design a registration
algorithm that can deal with topological changes for TBI patients.
One possible approach is to use combination of locally rigid and non-rigid transforms based on visual features such as MIND descriptor. Some regions of new lesions cannot be explained
by minor elastic registration, thus simultaneous registration and segmentation of lesions is
needed. We are working on the improved volume matching with boundary conditions based
on this segmentation.

= Key Investigators =

Georgia Tech: Yifei Lou and Patricio Vela

UAB: Arie Nakhmani and Allen Tannenbaum

UCLA: Andrei Irimia, Micah C. Chambers, Jack Van Horn and Paul M. Vespa

= References =
1.Yifei Lou, Andrei Irimia, Patricio Vela, Micah C. Chambers, Jack Van Horn, Paul M. Vespa and Allen Tannenbaum. Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes via the Bhattacharyya Distance. Submitted to IEEE Transactions on Bioengineering, 2012

2. Yifei Lou, Xun Jia, Xuejun Gu and Allen Tannenbaum. A GPU-based Implementation of Multimodal Deformable Image Registration Based on Mutual Information or Bhattacharyya Distance. Insight Journal, 2011. [http://www.midasjournal.org/browse/publication/803]

3. E. D’Agostino, F. Maes, D. Vandermeulen, and P. Suetens. A viscous fluid model for multimodal non-rigid image registration using mutual information,” MICCAI, 2002, pp. 541–548

4. M.P. Heinrich, M. Jenkinson, M. Bhushan, T. Matin, F. Gleeson, M. Brady, J.A. Schnabel. MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration. Medical Image Analysis. Vol. 16(7) Oct. 2012, pp. 1423–1435, Special Issue on MICCAI 2011

Projects:RegistrationTBI

2012-11-21T19:01:33Z

Anry: /* Future Work */

Back to [[Algorithm:BU|Boston University Algorithms]]
__NOTOC__
= Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units =

= Description =
An estimated 1.7 million Americans sustain traumatic brain injuries (TBI's) every year. The large number of recent TBI cases in soldiers returning from military conflicts has highlighted the critical need for improvement of TBI care and treatment, and has drawn sustained attention to the need for improved methodologies of TBI neuroimaging data analysis. Neuroimaging of TBI is vital for surgical planning by providing important information for anatomic localization and surgical navigation, as well as for monitoring patient case evolution over time. Approximately 2 days after the acute injury, magnetic resonance imaging (MRI) becomes preferable to computed tomography (CT) for the purpose of lesion characterization, and the use of various MR sequences tailored to capture distinct aspects of TBI pathology provides clinicians with essential complementary information for the assessment of TBI-related anatomical insults and pathophysiology.

Image registration plays an essential role in a wide variety of TBI data analysis workflows. It aims to find a transformation between two image sets such that the transformed image becomes similar to the target image according to some chosen metric or criterion. Typically, a similarity measure is first established to quantify how `close` two image volumes are to each other. Next, the transformation that maximizes this similarity is typically computed through an optimization process which constrains the transformation to a predetermined class, such as rigid, affine or deformable. Numerous challenges associated with the task of TBI volume co-registration can exist if data acquisition is performed multimodally, and additional complexities can also arise due to the large degree of algorithmic robustness that may be required in order to properly address pathology-related deformations. Many conventional methods use the sum of squared differences of intensity values between two image sets as a similarity measure, which can perform poorly or even fail for TBI volume registration. Consequently, because the deformation of patient anatomy and soft tissues cannot typically be represented by rigid transforms, image registration often requires deformable image registration (DIR), i.e., the necessity of applying nonparametric infinite-dimensional transformations.

This paper proposes to replace the Mutual Information (MI) criterion for registration with the Bhattachayya distance [1] within a multimodal DIR framework [3]. The advantage of BD over MI is the superior behavior of the square root function compared to that of the logarithm at zero, which yields a more stable algorithm.
This framework we describe takes into account the physical models of tissue motion to regularize the deformation fields and also involves free-form deformation. On the other hand, the DIR algorithm is computationally expensive when implemented on conventional central processing units, which can be detrimental particularly when three-dimensional (3D) volumes-rather than 2D images-need to be co-registered. In clinical settings that involve acute TBI care, the amount of time required by the processing of neuroimaging data sets from patients in critical condition should be minimized. To meet this clinical requirement, we have implemented our algorithm on a graphics processing unit (GPU) platform [2].

== Result ==

MR volumes were acquired at 3 T using a Siemens Trio TIM scanner (Siemens AG, Erlangen, Germany). Because assessing the time evolution of TBI between the acute to the chronic stage is of great interest in the clinical field in order to evaluate case evolution, scanning sessions were held both several days (acute baseline) as well as 6 months (chronic follow-up) after the traumatic injury event. To eliminate the effect of different scanner parameters during each scanning session, every subject was scanned using the same scanner for both acute and chronic time points. The MP-RAGE sequence (Mugler and Brookeman, 1990) was used to acquire T1-weighted images. In addition, MR data were also acquired using fluid-attenuated inversion recovery (FLAIR, (De Coene et al., 1992)), gradient-recalled echo (GRE) T2-weighted images as well as diffusion weighted imaging (DWI), and perfusion imaging.

Before applying our deformable registration algorithm, all image volumes were co-registered by rigid-body transformation to the pre-contrast T1-weighted volume acquired during the acute baseline scanning session. This helps to correct for head tilt and reduce error in computing the local deformation fields. Another technique that was employed before performing the registration is skull stripping, which was useful in our case because images acquired at the acute stage exhibit appreciably more extracranial swelling compared to images acquired chronically. Since all modalities are co-registered to T1, we only need to perform the skull stripping once, i.e. on the T1 volume. Skull stripping is necessary because, without it, the DIR algorithm would deform the interior of the brain to match the outside boundary. This type of deformation is mathematically valid, but does not yield anatomically plausible results. Two possible solutions to this problem are either adding prior knowledge on the boundary or applying skull stripping, of which we opt for the latter due to its common usage in image processing. We use the BrainSuit software [http://users.loni.ucla.edu/~shattuck/brainsuite/corticalsurface/bse/] for the skull stripping.

[[Image:ToT1e1.png|800px]]

Excluding preprocessing steps, the registration of two volumes of sizes 256x256x60 is found to require 6 seconds on the GPU. Registration results are illustrated for a 2D slice in the above figure for acute stage. The norm of the deformation fields and its 2D motion grid are also included. For T2, FLAIR and GRE volumes, the largest amount of deformation is observed bilaterally in the deep periventricular white matter, possibly as a consequence of hemorrhage and/or CSF infiltration into edemic regions, which can alter voxel intensities in GRE and FLAIR imaging, respectively. In the case of DWI, notable deformation is observed frontally and frontolaterally; in the former case, this may be the result of warping artifacts due to the large drop in the physical properties of tissues at the interfaces between brain, bone and air. In the latter case, the deformation is possibly due to the presence of TBI-related edema, which can substantially alter local diffusivity values. Similar effects due to these causes are observed with DWI and with perfusion imaging in both acute and chronic scans.

== Future Work ==

Future work will focus on registration of TBI volumes across time in terms of registering acute to chronic or vice versa. There are a large number of registration algorithms that assume the
smoothness of the vector flow, i.e., the deformation is
diffeomorphic. However, when registering TBI across time, the deformation is not well-defined, let alone
to be diffeomorphic, at some regions where bleeding or lesion
occurs. It is challenging and important to design a registration
algorithm that can deal with topological changes for TBI patients.
One possible approach is to use combination of locally rigid and non-rigid transforms based on visual features such as MIND descriptor. Some regions of new lesions cannot be explained
by minor elastic registration, thus simultaneous registration and segmentation of lesions is
needed. We are working on the improved volume matching with boundary conditions based
on this segmentation.

= Key Investigators =

Georgia Tech: Yifei Lou and Patricio Vela

UAB: Arie Nakhmani and Allen Tannenbaum

UCLA: Andrei Irimia, Micah C. Chambers, Jack Van Horn and Paul M. Vespa

= References =
1.Yifei Lou, Andrei Irimia, Patricio Vela, Micah C. Chambers, Jack Van Horn, Paul M. Vespa and Allen Tannenbaum. Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes via the Bhattacharyya Distance. Submitted to IEEE Transactions on Bioengineering, 2012

2. Yifei Lou, Xun Jia, Xuejun Gu and Allen Tannenbaum. A GPU-based Implementation of Multimodal Deformable Image Registration Based on Mutual Information or Bhattacharyya Distance. Insight Journal, 2011. [http://www.midasjournal.org/browse/publication/803]

3. E. D’Agostino, F. Maes, D. Vandermeulen, and P. Suetens. A viscous fluid model for multimodal non-rigid image registration using mutual information,” MICCAI, 2002, pp. 541–548

Projects:RegistrationTBI

2012-11-21T18:55:54Z

Anry: /* Key Investigators */

Back to [[Algorithm:BU|Boston University Algorithms]]
__NOTOC__
= Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units =

= Description =
An estimated 1.7 million Americans sustain traumatic brain injuries (TBI's) every year. The large number of recent TBI cases in soldiers returning from military conflicts has highlighted the critical need for improvement of TBI care and treatment, and has drawn sustained attention to the need for improved methodologies of TBI neuroimaging data analysis. Neuroimaging of TBI is vital for surgical planning by providing important information for anatomic localization and surgical navigation, as well as for monitoring patient case evolution over time. Approximately 2 days after the acute injury, magnetic resonance imaging (MRI) becomes preferable to computed tomography (CT) for the purpose of lesion characterization, and the use of various MR sequences tailored to capture distinct aspects of TBI pathology provides clinicians with essential complementary information for the assessment of TBI-related anatomical insults and pathophysiology.

Image registration plays an essential role in a wide variety of TBI data analysis workflows. It aims to find a transformation between two image sets such that the transformed image becomes similar to the target image according to some chosen metric or criterion. Typically, a similarity measure is first established to quantify how `close` two image volumes are to each other. Next, the transformation that maximizes this similarity is typically computed through an optimization process which constrains the transformation to a predetermined class, such as rigid, affine or deformable. Numerous challenges associated with the task of TBI volume co-registration can exist if data acquisition is performed multimodally, and additional complexities can also arise due to the large degree of algorithmic robustness that may be required in order to properly address pathology-related deformations. Many conventional methods use the sum of squared differences of intensity values between two image sets as a similarity measure, which can perform poorly or even fail for TBI volume registration. Consequently, because the deformation of patient anatomy and soft tissues cannot typically be represented by rigid transforms, image registration often requires deformable image registration (DIR), i.e., the necessity of applying nonparametric infinite-dimensional transformations.

This paper proposes to replace the Mutual Information (MI) criterion for registration with the Bhattachayya distance [1] within a multimodal DIR framework [3]. The advantage of BD over MI is the superior behavior of the square root function compared to that of the logarithm at zero, which yields a more stable algorithm.
This framework we describe takes into account the physical models of tissue motion to regularize the deformation fields and also involves free-form deformation. On the other hand, the DIR algorithm is computationally expensive when implemented on conventional central processing units, which can be detrimental particularly when three-dimensional (3D) volumes-rather than 2D images-need to be co-registered. In clinical settings that involve acute TBI care, the amount of time required by the processing of neuroimaging data sets from patients in critical condition should be minimized. To meet this clinical requirement, we have implemented our algorithm on a graphics processing unit (GPU) platform [2].

== Result ==

MR volumes were acquired at 3 T using a Siemens Trio TIM scanner (Siemens AG, Erlangen, Germany). Because assessing the time evolution of TBI between the acute to the chronic stage is of great interest in the clinical field in order to evaluate case evolution, scanning sessions were held both several days (acute baseline) as well as 6 months (chronic follow-up) after the traumatic injury event. To eliminate the effect of different scanner parameters during each scanning session, every subject was scanned using the same scanner for both acute and chronic time points. The MP-RAGE sequence (Mugler and Brookeman, 1990) was used to acquire T1-weighted images. In addition, MR data were also acquired using fluid-attenuated inversion recovery (FLAIR, (De Coene et al., 1992)), gradient-recalled echo (GRE) T2-weighted images as well as diffusion weighted imaging (DWI), and perfusion imaging.

Before applying our deformable registration algorithm, all image volumes were co-registered by rigid-body transformation to the pre-contrast T1-weighted volume acquired during the acute baseline scanning session. This helps to correct for head tilt and reduce error in computing the local deformation fields. Another technique that was employed before performing the registration is skull stripping, which was useful in our case because images acquired at the acute stage exhibit appreciably more extracranial swelling compared to images acquired chronically. Since all modalities are co-registered to T1, we only need to perform the skull stripping once, i.e. on the T1 volume. Skull stripping is necessary because, without it, the DIR algorithm would deform the interior of the brain to match the outside boundary. This type of deformation is mathematically valid, but does not yield anatomically plausible results. Two possible solutions to this problem are either adding prior knowledge on the boundary or applying skull stripping, of which we opt for the latter due to its common usage in image processing. We use the BrainSuit software [http://users.loni.ucla.edu/~shattuck/brainsuite/corticalsurface/bse/] for the skull stripping.

[[Image:ToT1e1.png|800px]]

Excluding preprocessing steps, the registration of two volumes of sizes 256x256x60 is found to require 6 seconds on the GPU. Registration results are illustrated for a 2D slice in the above figure for acute stage. The norm of the deformation fields and its 2D motion grid are also included. For T2, FLAIR and GRE volumes, the largest amount of deformation is observed bilaterally in the deep periventricular white matter, possibly as a consequence of hemorrhage and/or CSF infiltration into edemic regions, which can alter voxel intensities in GRE and FLAIR imaging, respectively. In the case of DWI, notable deformation is observed frontally and frontolaterally; in the former case, this may be the result of warping artifacts due to the large drop in the physical properties of tissues at the interfaces between brain, bone and air. In the latter case, the deformation is possibly due to the presence of TBI-related edema, which can substantially alter local diffusivity values. Similar effects due to these causes are observed with DWI and with perfusion imaging in both acute and chronic scans.

== Future Work ==

Future work will focus on registration of TBI volumes across time in terms of registering acute to chronic or vice versa. There are a large number of registration algorithms that assume the
smoothness of the vector flow, i.e., the deformation is
diffeomorphic. However, when registering TBI across time, the deformation is not well-defined, let along
to be diffeomorphic, at some regions where bleeding or lesion
occurs. It is challenging and important to design a registration
algorithm that can deal with topological changes for TBI patients.

= Key Investigators =

Georgia Tech: Yifei Lou and Patricio Vela

UAB: Arie Nakhmani and Allen Tannenbaum

UCLA: Andrei Irimia, Micah C. Chambers, Jack Van Horn and Paul M. Vespa

= References =
1.Yifei Lou, Andrei Irimia, Patricio Vela, Micah C. Chambers, Jack Van Horn, Paul M. Vespa and Allen Tannenbaum. Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes via the Bhattacharyya Distance. Submitted to IEEE Transactions on Bioengineering, 2012

2. Yifei Lou, Xun Jia, Xuejun Gu and Allen Tannenbaum. A GPU-based Implementation of Multimodal Deformable Image Registration Based on Mutual Information or Bhattacharyya Distance. Insight Journal, 2011. [http://www.midasjournal.org/browse/publication/803]

3. E. D’Agostino, F. Maes, D. Vandermeulen, and P. Suetens. A viscous fluid model for multimodal non-rigid image registration using mutual information,” MICCAI, 2002, pp. 541–548

Algorithm:BU

2012-11-21T18:55:01Z

Anry: /* Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =

At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Boston University/UAB Projects =

{| cellpadding="10" style="text-align:left;"

| | [[Image:LiverFibrosisHist.png|200px|]]
| |

== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==

This project is conducted in collaboration with the Boston Medical Center. We are trying
to provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data
has much lower resolution than histological image data, but it includes the entire liver
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)
in diﬀusion MRI correlates with the ﬁbrosis stage. [[Projects:LiverFibrosisStaging|More...]]

|-

| | [[Image:Heart_topology.jpg|200px|]]
| |

== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]

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| | [[Image:toT1e1.png|200px|]]
| |

== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==

Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In this method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension
to the principal axis transformation method for ﬁnding robust rigid transformation of two
volumes. The additional elastic registration is based on a volume registration method
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]

|-

| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]
| |

== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==

We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured. [[Projects:MultiScaleShapeSegmentation|More...]]

<font color="red">'''New: '''</font> Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.

|-

| | [[Image:3D_Segmentation_LA.png|200px]]
| |

== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

<font color="red">'''New: '''</font> Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.
|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.

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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.

<font color="red">'''New: '''</font> Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .

|-

| | [[File:LASegAxialView.png|200px]]
| |

== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==

The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), in submission.

<font color="red">'''New: '''</font>L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

|-

| | [[Image:ScarSeg_EM.png|200px]]
| |

== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]

<font color="red">'''New: '''</font> Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.

|-

| | [[Image:LongitudinalAFib.png|200px]]
| |

== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]

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| | [[Image:LRV_Wall.png|200px]]
| |

== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.

|-

| | [[Image:RiskMassSeg.png|200px]]
| |

== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|300px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

<font color="red">'''New: '''</font> Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation. Neuroimage, volume 45, 2009, pp. 123-132.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)

|-

| | [[Image:KVoutSegTightMod.png|200px]]
| |

== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==

An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]

<font color="red">'''New: '''</font> I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.

<font color="red">'''New: '''</font> P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.

|-

| | [[Image:PostRegFleshSkeleton.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==

A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]

<font color="red">'''New: '''</font> I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

|-

| | [[Image:Model3D_upTrans.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.

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| | [[Image:Circle seg.PNG|200px|]]
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== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115.

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| | [[Image:ZoomedResultWithModel.png|200px]]
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.

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| | [[Image:P1_small.png|200px|]]
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.

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| | [[Image:BasePair3DModel.JPG|200px|]]
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

<font color="red">'''New: '''</font> X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.

<font color="red">'''New: '''</font> Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.

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| | [[Image:TruckInitialization.png|200px|]]
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.

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| | [[Image:Results brain sag.JPG|200px]]
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==

The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.

<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.

<font color="red">'''New: '''</font> Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.

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| | [[Image:Gatech caudateBands.PNG|200px]]
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

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| | [[Image:GT-SPD-img1.png|200px|]]
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

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| | [[Image:Basis membership.png|200px]]
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

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| | [[Image:Striatum1.png|200px|]]
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

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| | [[Image:Brain-flat.PNG|200px]]
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

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| | [[Image:Fig67.png|200px|]]
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

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| | [[Image:Stochastic-snake.png|200px|]]
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

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| | [[Image:GT-SulciOutlining1.jpg|200px]]
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

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| | [[Image:Table1.png|200px|]]
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

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| | [[Image:Gatech SlicerModel2.jpg|200px]]
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

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Algorithm:BU

2012-11-21T18:54:03Z

Anry: /* Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =

At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Boston University/UAB Projects =

{| cellpadding="10" style="text-align:left;"

| | [[Image:LiverFibrosisHist.png|200px|]]
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== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==

This project is conducted in collaboration with the Boston Medical Center. We are trying
to provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data
has much lower resolution than histological image data, but it includes the entire liver
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)
in diﬀusion MRI correlates with the ﬁbrosis stage. [[Projects:LiverFibrosisStaging|More...]]

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| | [[Image:Heart_topology.jpg|200px|]]
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== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]

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| | [[Image:toT1e1.png|200px|]]
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes]] ==

Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problem using an implementation of multimodal deformable registration methods. One method have been implemented on graphics processing units (GPU). In these method we follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. Another method proposes an extension
to the principal axis transformation method for ﬁnding robust rigid transformation of two
volumes. The additional elastic registration is based on a volume registration method
MIND, proposed recently by Heinrich et al. [[Projects:RegistrationTBI|More...]]

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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==

We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured. [[Projects:MultiScaleShapeSegmentation|More...]]

<font color="red">'''New: '''</font> Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.

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| | [[Image:3D_Segmentation_LA.png|200px]]
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== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

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| | [[Image:Pain1.JPG|200px]]
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

<font color="red">'''New: '''</font> Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.

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| | [[Image:MultiObjSeg.png|200px|]]
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== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.

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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.

<font color="red">'''New: '''</font> Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .

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| | [[File:LASegAxialView.png|200px]]
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== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==

The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), in submission.

<font color="red">'''New: '''</font>L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

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| | [[Image:ScarSeg_EM.png|200px]]
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== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]

<font color="red">'''New: '''</font> Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.

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| | [[Image:LongitudinalAFib.png|200px]]
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== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]

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| | [[Image:LRV_Wall.png|200px]]
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== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.

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| | [[Image:RiskMassSeg.png|200px]]
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== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.

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| | [[Image:GT-DWI-Reorientation-1.jpg|300px]]
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

<font color="red">'''New: '''</font> Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation. Neuroimage, volume 45, 2009, pp. 123-132.

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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.

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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)

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| | [[Image:KVoutSegTightMod.png|200px]]
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== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==

An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]

<font color="red">'''New: '''</font> I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.

<font color="red">'''New: '''</font> P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.

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| | [[Image:PostRegFleshSkeleton.png|200px]]
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== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==

A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]

<font color="red">'''New: '''</font> I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

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| | [[Image:Model3D_upTrans.png|200px]]
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115.

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.

|-

| | [[Image:P1_small.png|200px|]]
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.

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| | [[Image:BasePair3DModel.JPG|200px|]]
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

<font color="red">'''New: '''</font> X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.

<font color="red">'''New: '''</font> Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.

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| | [[Image:TruckInitialization.png|200px|]]
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.

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| | [[Image:Results brain sag.JPG|200px]]
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==

The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.

<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.

<font color="red">'''New: '''</font> Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

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| | [[Image:GT-SPD-img1.png|200px|]]
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

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| | [[Image:Basis membership.png|200px]]
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

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| | [[Image:Dlpfc1.jpg|200px|]]
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

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| | [[Image:Striatum1.png|200px|]]
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
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== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

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| | [[Image:Fig1yan.PNG|200px|]]
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

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| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

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| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Projects:LiverFibrosisStaging

2012-11-21T18:38:51Z

Anry: /* Segmentation */

= Liver Fibrosis Staging =

This project is conducted in collaboration with the Boston Medical Center. We are trying to provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous. Moreover, the decision of the pathologist based on a biopsy is subjective, and depends on the sample, because the ﬁbrosis level varies along the liver. No objective standard has been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data has much lower resolution than histological image data, but it includes the entire liver volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool. Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC) in diﬀusion MRI correlates with the ﬁbrosis stage.

== Our Approach ==

In our research, we have tested different ADC image texture features, and have found features
(e.g., standard deviation of color distribution) that correlate with histological results much better than average
brightness. Using the tools from visual tracking research, we have developed algorithm for
automatic hepatic MRI segmentation, and algorithm for automatic liver staging, based on
optimal feature grouping. In addition, we are developing novel topological continuous features to improve the classification.

== Results ==

The original liver slice for b=4 is shown in the Figure 1. The automatically segmented slice converted to Apparent Diffusion Coefficient (ADC) image, and shown in the Figure 2. Using multiple ADC image features we are able to provide automatic grading of hepatic fibrosis which correlate more than 80% with the histopathology results. This result improves the current state-of-the-art algorithms that able to classify livers only to two groups (healthy/unhealthy), with the similar correlation.

<gallery>
Image:LiverDiffMRI.png|Figure 1. The original MRI data.
Image:LiverDiffMRI_Segmented.png|Figure 2. Automatically segmented MRI ADC.
</gallery>

= Publications =

Nakhmani A., Anderson. S., Tannenbaum A., "Automatic Segmentation and Classification of Hepatic Fibrosis Grade Using Diffusion MRI," In preparation.

= Key Investigators =

* UAB: Arie Nakhmani and Allen Tannenbaum

Projects:LiverFibrosisStaging

2012-11-21T18:36:49Z

Anry: Created page with '= Segmentation = This project is conducted in collaboration with the Boston Medical Center. We are trying to provide tools for robust liver ﬁbrosis staging, based on MRI image…'

= Segmentation =

This project is conducted in collaboration with the Boston Medical Center. We are trying to provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous. Moreover, the decision of the pathologist based on a biopsy is subjective, and depends on the sample, because the ﬁbrosis level varies along the liver. No objective standard has been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data has much lower resolution than histological image data, but it includes the entire liver volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool. Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC) in diﬀusion MRI correlates with the ﬁbrosis stage.

== Our Approach ==

In our research, we have tested different ADC image texture features, and have found features
(e.g., standard deviation of color distribution) that correlate with histological results much better than average
brightness. Using the tools from visual tracking research, we have developed algorithm for
automatic hepatic MRI segmentation, and algorithm for automatic liver staging, based on
optimal feature grouping. In addition, we are developing novel topological continuous features to improve the classification.

== Results ==

The original liver slice for b=4 is shown in the Figure 1. The automatically segmented slice converted to Apparent Diffusion Coefficient (ADC) image, and shown in the Figure 2. Using multiple ADC image features we are able to provide automatic grading of hepatic fibrosis which correlate more than 80% with the histopathology results. This result improves the current state-of-the-art algorithms that able to classify livers only to two groups (healthy/unhealthy), with the similar correlation.

<gallery>
Image:LiverDiffMRI.png|Figure 1. The original MRI data.
Image:LiverDiffMRI_Segmented.png|Figure 2. Automatically segmented MRI ADC.
</gallery>

= Publications =

Nakhmani A., Anderson. S., Tannenbaum A., "Automatic Segmentation and Classification of Hepatic Fibrosis Grade Using Diffusion MRI," In preparation.

= Key Investigators =

* UAB: Arie Nakhmani and Allen Tannenbaum

File:LiverDiffMRI Segmented.png

2012-11-21T18:33:25Z

Anry:

File:LiverDiffMRI.png

2012-11-21T18:33:04Z

Anry:

Algorithm:BU

2012-11-21T18:10:47Z

Anry: /* Boston University/UAB Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =

At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Boston University/UAB Projects =

{| cellpadding="10" style="text-align:left;"

| | [[Image:LiverFibrosisHist.png|200px|]]
| |

== [[Projects:LiverFibrosisStaging|Liver Fibrosis Staging by MRI Analysis]] ==

This project is conducted in collaboration with the Boston Medical Center. We are trying
to provide tools for robust liver ﬁbrosis staging, based on MRI image analysis. The current
practice of ﬁbrosis assessment, which is based on painful liver biopsy, might be dangerous.
Moreover, the decision of the pathologist based on a biopsy is subjective, and depends
on the sample, because the ﬁbrosis level varies along the liver. No objective standard has
been developed yet for histological ﬁbrosis assessment. Magnetic resonance volume data
has much lower resolution than histological image data, but it includes the entire liver
volume. Also, MRI is non-invasive and not painful, thus it is preferred as a diagnostic tool.
Previously it has been hypothesized that the average brightness of Apparent Diﬀusion Coeﬃcient (ADC)
in diﬀusion MRI correlates with the ﬁbrosis stage. [[Projects:LiverFibrosisStaging|More...]]

|-

| | [[Image:Heart_topology.jpg|200px|]]
| |

== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]

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| | [[Image:toT1e1.png|200px|]]
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== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==

Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]

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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]
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== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==

We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured. [[Projects:MultiScaleShapeSegmentation|More...]]

<font color="red">'''New: '''</font> Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.

|-

| | [[Image:3D_Segmentation_LA.png|200px]]
| |

== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

<font color="red">'''New: '''</font> Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.
|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.

<font color="red">'''New: '''</font> Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .

|-

| | [[File:LASegAxialView.png|200px]]
| |

== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==

The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), in submission.

<font color="red">'''New: '''</font>L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

|-

| | [[Image:ScarSeg_EM.png|200px]]
| |

== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]

<font color="red">'''New: '''</font> Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.

|-

| | [[Image:LongitudinalAFib.png|200px]]
| |

== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]

|-

| | [[Image:LRV_Wall.png|200px]]
| |

== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.

|-

| | [[Image:RiskMassSeg.png|200px]]
| |

== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|300px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

<font color="red">'''New: '''</font> Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation. Neuroimage, volume 45, 2009, pp. 123-132.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)

|-

| | [[Image:KVoutSegTightMod.png|200px]]
| |

== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==

An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]

<font color="red">'''New: '''</font> I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.

<font color="red">'''New: '''</font> P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.

|-

| | [[Image:PostRegFleshSkeleton.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==

A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]

<font color="red">'''New: '''</font> I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

|-

| | [[Image:Model3D_upTrans.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115.

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

<font color="red">'''New: '''</font> X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.

<font color="red">'''New: '''</font> Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==

The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.

<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.

<font color="red">'''New: '''</font> Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:GT-SPD-img1.png|200px|]]
| |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

File:LiverFibrosisHist.png

2012-11-21T17:51:17Z

Anry:

Projects:TopologicalSegmentation

2012-11-21T17:42:34Z

Anry: /* Our Approach */

= Segmentation =

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical.

== Our Approach ==

We are exploring different image features that are appropriate for such automatic segmentation tasks.
Our objective is to develop and improve topological extrema ranking
algorithms (e.g., maximum persistence, and perceptual ridge importance) to provide the
most appropriate features, and to compare the results to expert’s manual segmentation.

Based on the obtained edge or ridge features, we are using two different approaches to create a contour.
The first uses a novel boundary linking algorithm where all the detected ridges converted to 8-point extrema vector,
and then the connections between the ridges are made using these vectors. The second approach uses active contours to provide smoother results.

== Results ==

The example of extracted ridges is shown in the Figure 1. The connected boundary is shown in the Figure 2. For the tested volumes, when comparing the obtained boundary with expert's manual segmentation, the average distance error is about 2 pixels. The results of GVF active contour approach are shown in the Figures 3-5. The bright gray contour is the expert's segmentation, and yellow contour is the result of our algorithm. The average distance error there is less than 2 pixels.

<gallery>
Image:Ridge.png|Figure 1. Topologically important ridge structures
Image:Connected_ridge.png|Figure 2. Connected boundary
Image:Heart1topology.png|Figure 3. Frame 15.
Image:Heart2topology.png|Figure 4. Frame 17.
Image:Heart3topology.png|Figure 5. Frame 25.
</gallery>

== Project Week ==

[http://www.na-mic.org/Wiki/index.php/2012_Summer_Project_Week:RidgeExtractionAtrialWallSegmentation Summer Project Week 2012]

= Publications =

Nakhmani A., Tannenbaum A., "Automatic Segmentation of Left Atrium Wall Using Topologically Significant Edge Features," In preparation.

= Key Investigators =

* UAB: Arie Nakhmani and Allen Tannenbaum

Projects:TopologicalSegmentation

2012-11-21T17:39:17Z

Anry: Created page with '= Segmentation = Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI data, obtained at University of Utah, are used to explore lesion ablat…'

= Segmentation =

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical.

== Our Approach ==

We are exploring different image features that are appropriate for such automatic segmentation tasks.
Our objective is to develop and improve topological extrema ranking
algorithms (e.g., maximum persistence, and perceptual ridge importance) to provide the
most appropriate features, and to compare the results to expert’s manual segmentation.

Based on the obtained edge or ridge features, we are use two different approaches to create a contour.
The first uses a novel boundary linking algorithm where all the detected ridges converted to 8-point extrema vector,
and then the connections between ridges are made using these vectors. The second approach uses active contours to provide smoother results.

== Results ==

The example of extracted ridges is shown in the Figure 1. The connected boundary is shown in the Figure 2. For the tested volumes, when comparing the obtained boundary with expert's manual segmentation, the average distance error is about 2 pixels. The results of GVF active contour approach are shown in the Figures 3-5. The bright gray contour is the expert's segmentation, and yellow contour is the result of our algorithm. The average distance error there is less than 2 pixels.

<gallery>
Image:Ridge.png|Figure 1. Topologically important ridge structures
Image:Connected_ridge.png|Figure 2. Connected boundary
Image:Heart1topology.png|Figure 3. Frame 15.
Image:Heart2topology.png|Figure 4. Frame 17.
Image:Heart3topology.png|Figure 5. Frame 25.
</gallery>

== Project Week ==

[http://www.na-mic.org/Wiki/index.php/2012_Summer_Project_Week:RidgeExtractionAtrialWallSegmentation Summer Project Week 2012]

= Publications =

Nakhmani A., Tannenbaum A., "Automatic Segmentation of Left Atrium Wall Using Topologically Significant Edge Features," In preparation.

= Key Investigators =

* UAB: Arie Nakhmani and Allen Tannenbaum

File:Heart3topology.png

2012-11-21T17:34:12Z

Anry:

File:Heart2topology.png

2012-11-21T17:33:54Z

Anry:

File:Heart1topology.png

2012-11-21T17:21:21Z

Anry:

File:Connected ridge.png

2012-11-21T17:19:17Z

Anry:

File:Ridge.png

2012-11-21T17:16:46Z

Anry:

Algorithm:BU

2012-11-21T15:39:40Z

Anry: /* Boston University/UAB Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Boston University Algorithms (PI: Allen Tannenbaum) =

At Boston University and the Comprehensive Cancer Center of UAB, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Boston University/UAB Projects =

{| cellpadding="10" style="text-align:left;"

| | [[Image:Heart_topology.jpg|200px|]]
| |

== [[Projects:TopologicalSegmentation|Left Atrium Wall Segmentation Using Topological Features]] ==

Catheter ablation has been proposed for treatment of atrial ﬁbrillation arrhythmia. MRI
data, obtained at University of Utah, are used to explore lesion ablation and scariﬁcation
locations. In addition, MRI analysis may help to predict if the ablation procedure will help
a patient or not. Many of these image analysis tasks are largely based on segmentation of
left atrial wall, which is done manually or semi-automatically. Automatic segmentation uses
moving contours or surfaces (interfaces) to segment image data by minimizing a predeﬁned
energy function. These moving interfaces are highly aﬀected by image data, which can be
thought as a force ﬁeld pushing the interface to features of choice. Thus, the choice of
interface attracting image features is critical. [[Projects:TopologicalSegmentation|More...]]

|-

| | [[Image:toT1e1.png|200px|]]
| |

== [[Projects:RegistrationTBI|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] ==

Time-efficient processing and analysis of magnetic resonance imaging (MRI) volumes is desirable is for the neurocritical care and monitoring of traumatic brain injury (TBI) patients. An important problem of TBI neuroimaging data analysis is the task of co-registering MR volumes acquired using distinct sequences in the presence of widely variable pixel intensities that are due to the presence of pathology. Here we address this important and challenging problems using an implementation of multimodal deformable registration on graphics processing units (GPU). We follow a viscous fluid model framework and replace mutual information with the Bhattacharyya distance as the measure of similarity between image volumes. The proposed algorithm is implemented on a GPU and its robustness is illustrated using a longitudinal multimodal TBI dataset. [[Projects:RegistrationTBI|More...]]

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| | [[Image:MultiScaleHippoSegmentationHausdorf.png|200px]]
| |

== [[Projects:MultiScaleShapeSegmentation|Multi-scale Shape Representation, Registration, and Segmentation With Applications to Radiotherapy]] ==

We present in this work a multiscale representation for shapes with arbitrary topology, and a method to segment the target organ/tissue from medical images having very low contrast with respect to surrounding regions using multiscale shape information and local image features. In a number of previous papers, shape knowledge was incorporated by first constructing a shape space from training data, and then constraining the segmentation process to be within the resulting shape space. However, such an approach has certain limitations including the fact that small scale shape variances may be overwhelmed by those on larger scale, and therefore the local shape information is lost. In this work, first we handle this problem by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances captured by the statistical learning step are also represented at various scales. In doing so, not only is the diversity of shape enriched, but also small scale changes are nicely captured. [[Projects:MultiScaleShapeSegmentation|More...]]

<font color="red">'''New: '''</font> Yifei Lou, Tianye Niu, Xun Jia, Patricio Vela, Lei Zhu, Allen Tannenbaum, Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy, MedIA (in submission), 2012.

|-

| | [[Image:3D_Segmentation_LA.png|200px]]
| |

== [[Projects:AFibSegmentationRegistration|Segmentation and Registration for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. Tannenbaum, Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging. IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466, 2012.

<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging. Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

<font color="red">'''New: '''</font> Wassim M. Haddad, James M. Bailey, Behnood Gholami, and Allen Tannenbaum. Optimal Drug Dosing Control for Intensive Care Unit Sedation Using a Hybrid Deterministic-Stochastic Pharmacokinetic and Pharmacodynamic
Model. Optimal Control, Applications and Methods}, 2012, DOI: 10.1002/oca.2038.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, S. Bouix, M. Shenton, R. Kikinis, A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. MedIA, volume 16, 2012, pp. 1216-1227.
|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans. Medical Imaging, volume 29, 2010, pp. 1781-1794.

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI vol.29, 2010, pp. 1781-1794.

<font color="red">'''New: '''</font> Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the diffeomorphism group and the registration of point sets. IEEE Transactions Image Processing, vol. 21, 2012, pp. 4383-4396 .

|-

| | [[File:LASegAxialView.png|200px]]
| |

== [[Projects:LeftAtriumSegmentation|Left Atrium Segmentation for Atrial Fibrillation Treatment]] ==

The planning and evaluation of left atrial ablation procedures is commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical
variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery (MRI). The segmentation problem is formulated as a problem in variational region growing. [[Projects:LeftAtriumSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI images using Variational Region Growing with a Shape Prior, IEEE Transaction on Medical Imaging(TMI), in submission.

<font color="red">'''New: '''</font>L. Zhu, Y. Gao, A. Yezzi, R. MacLeod, J. Cates, A. Tannenbaum. Automatic Segmentation of the Left Atrium from MRI Images Using Salient Feature and Contour Evolution, IEEE Engineering in Medicine and Biology Conference(EMBC), 2012.

|-

| | [[Image:ScarSeg_EM.png|200px]]
| |

== [[Projects:ScarIdentification|Scar Tissue Identification for Post-Ablation Analysis]] ==
The delay-enhanced MRI (DE-MRI) technique provides an effective way of imaging scarring and fibrosis tissue of atria. Segmentation of the LA from DE-MRI images can
be used in atrial fibrillation (AF) treatment to select suitable candidates for ablation therapy and subsequent monitoring of the therapy. [[Projects:ScarIdentification|More...]]

<font color="red">'''New: '''</font> Y. Gao, L. Zhu, A. Yezzi, S. Bouix , A. Tannenbaum. Scar Segmentation in DE-MRI, IEEE International Symposium on Biomedical Imaging (ISBI) , 2012.

|-

| | [[Image:LongitudinalAFib.png|200px]]
| |

== [[Projects:AFibLongitudinalAnalysis|Longitudinal Shape Analysis for AFib]] ==
The shape evolution of the left atrium in the atrial fibrillation patiens is studied longitudinally to reveal the difference between recover group and the AFib recurrence group. [[Projects:AFibLongitudinalAnalysis|More...]]

|-

| | [[Image:LRV_Wall.png|200px]]
| |

== [[Projects:VentricleSegmentation|Ventricles Segmentation for Diagnosis of Cardiac Diseases]] ==
This work presents an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ven-
tricles are located sequentially, in which each ventricle is detected by first identifying the endocardial surface and then segmenting the epicardial surface. [[Projects:VentricleSegmentation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Appia, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. Automatic Extraction of the Myocardial Wall from CT Images using Shape Segmentation and Variational Region Growing, IEEE Transaction on Biomedical Engineering(TBME), In preparation.

|-

| | [[Image:RiskMassSeg.png|200px]]
| |

== [[Projects:RiskMassEstimation|Risk Mass Estimation for Heart Risk Evaluation]] ==
Prognosis and treatment of cardiovascular diseases frequently require the determination of the myocardial mass at risk caused by coronary stenoses. However, few work has been done for estimating the myocardial mass at risk directly from the heart surface segmented from CAT imagery, rather than using a simplified heart model such as ellipsoid. [[Projects:RiskMassEstimation|More...]]

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, A. Yezzi, C. Arepalli , A. Stillman, and A. Tannenbaum. A Computational Framework for Estimating the Mass at Risk Caused by Stenoses using CT Angiography, Internatial Journal of Cardiac Imaging(IJCI), In preparation.

<font color="red">'''New: '''</font> L. Zhu, Y. Gao, V. Mohan, A. Stillman, T. Faber, A. Tannenbaum. Estimation of myocardial volume at risk from CT angiography, Proceedings of SPIE , pp.79632-38A, 2011.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|300px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

<font color="red">'''New: '''</font> Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation. Neuroimage, volume 45, 2009, pp. 123-132.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery, IEEE Transactions on Medical Imaging, volume 29, 2011, pp. 1945-1958.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission)

|-

| | [[Image:KVoutSegTightMod.png|200px]]
| |

== [[Projects:InteractiveSegmentation|Interactive Image Segmentation With Active Contours]] ==

An approach for tightly coupling the user into a semi-automatic segmentation framework is proposed in this work. A human guides the automatic segmentation by iteratively providing input until convergence to the desired segmentation. The result is a segmentation of manual quality in a fraction of the time; the whole process is intuitive and highly flexible [[Projects:InteractiveSegmentation|More...]]

<font color="red">'''New: '''</font> I. Kolesov, P.Karasev, G.Muller, K.Chudy, J.Xerogeanes, and A. Tannenbaum. Human Supervisory Control Framework for Interactive Medical Image Segmentation. MICCAI Workshop on Computational Biomechanics for Medicine 2011.

<font color="red">'''New: '''</font> P.Karasev, I.Kolesov, K.Chudy, G.Muller, J.Xerogeanes, and A. Tannenbaum. Interactive MRI Segmentation with Controlled Active Vision. IEEE CDC-ECC 2011.

|-

| | [[Image:PostRegFleshSkeleton.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-PtSetReg|Constrained Registration for Adaptive Radiotherapy]] ==

A hierarchical approach is described to register two CT scans from different patients. The registration process extracts point clouds representing anatomical structures and aligns them sequentially. The proposed method for registering point clouds can incorporate a variety of constraints including restriction on the injectivity of the deformation field and stationarity of selected landmarks. [[Projects:MGH-HeadAndNeck-PtSetReg|More...]]

<font color="red">'''New: '''</font> I. Kolesov, J. Lee, P.Vela, G. Sharp and A. Tannenbaum. Diffeomorphic Point Set Registration with Landmark Constraints. In Preparation for PAMI.

|-

| | [[Image:Model3D_upTrans.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for Head, Neck and Thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel Methods for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning nonlinear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, and A. Tannenbaum. A Non-Rigid Kernel Based Framework for 2D/3D Pose Estimation and 2D Image Segmentation. IEEE Trans Pattern Anal Mach Intelligence, volume 33, 2011, pp. 1098-1115.

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenent, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30, 2008, pp. 412-423.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, S. Bouix, M. Shenton, A. Tannenbaum. Affine registration of label maps in label space. Journal of Computing, volume 2, 2010, pp, 1-11.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

<font color="red">'''New: '''</font> X. LeFaucheur, E. Hershkovits, R. Tannenbaum, and A. Tannenbaum. Non-parametric clustering for studying RNA conformations. IEEE Trans. Computational Biology and Bioinformatics, volume 8, 2011, pp. 1604-1618.

<font color="red">'''New: '''</font> Il Tae Kim, A. Tannenbaum, R. Tannenbaum. Anisotropic conductivity of magnetic carbon nanotubes
embedded in epoxy matrices. Carbon, volume 49, 2011, pp. 54-61.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Point set registration via particle filtering and stochastic dynamics. IEEE TPAMI, volume 32, 2010, pp. 1459-1473.

<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. A non-rigid kernel based framework for 2D/3D pose estimation and 2D image segmentation. IEEE TPAMI, volume 33, 2011, pp. 1098-1115.

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration and Visualization]] ==

The goal of this project is to implement a computationally efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. SIAM Journal of Scientific Computing, volume 32, 2011, pp. 197-211.

<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. 3D nonrigid registration via optimal mass transport on the GPU. MedIA, volume 13, 2010, pp. 931-940.

<font color="red">'''New: '''</font> Ayelet Dominitz and Allen Tannenbaum. Texture Mapping Via Optimal Mass Transport. IEEE TVCG, volume 16, 2010), pp. 419-433.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:GT-SPD-img1.png|200px|]]
| |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Surface Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

File:Heart topology.jpg

2012-11-21T15:31:01Z

Anry:

2012 Summer Project Week:RidgeExtractionAtrialWallSegmentation

2012-06-22T13:01:02Z

Anry: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-MIT2012.png|[[2012_Summer_Project_Week#Projects|Projects List]]
Image:MRI1.jpg|Example of manual segmentation of the left atrial wall.
Image:Pic2.jpg|Polar transform of the left atrial wall.
Image:Pic3.jpg|Initial segmentation results.
</gallery>

==Background==
Catheter ablation has been proposed for treatment of atrial fibrillation arrhythmia. MRI data is used to explore lesion ablation and scarification locations are extracted from MRI image data. In addition, MRI analysis may help to predict if the ablation procedure will help a patient or not.
Many of these image analysis tasks are largely based on segmentation of left atrial wall, which is done manually or semi-automatically.
Automatic segmentation uses moving contours or surfaces (interfaces) to segment image data by minimizing a predefined energy function.
These moving interfaces are highly affected by image data, which can be thought as a force field pushing the interface to features of choice. Thus, the choice of interface attracting image features is critical.

==Key Investigators==
* BU: Arie Nakhmani
* BU and CCC UAB: Allen Tannenbaum

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are exploring different image features which are appropriate for an automatic segmentation. Our objective is to compare different topological extrema ranking algorithms (including maximum persistence [1,2] and perceptual ridge importance) to provide the most appropriate features, based on expert's manual segmentation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

* Implementing the comparison framework
* Ellipsoidal MRI transform
* Local ridge extraction
* Topological persistence algorithm implementation
* Comparison of different topological simplification approaches

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
We have developed topological simplification methods for removing noisy local maxima. The resulting ridge detection algorithm shows average error less then 2 pixels compared to the manual segmentation.

This week:
* We have developed general error computation framework for segmentation features quantitative comparison.
* Two topological simplification algorithms were implemented and compared to a regular edge base segmentation. Both implemented algorithms show about 40% reduction in error.
* In the future, we plan to compare more topological simplifications to improve the error.

</div>
</div>

==References==
# Edelsbrunner, H., Harer, J., & Zomorodian, A. (2003). Hierarchical Morse--Smale Complexes for Piecewise Linear 2-Manifolds. Discrete and Computational Geometry, 30(1), 87-107. doi:10.1007/s00454-003-2926-5
# Szymczak, A., Stillman, A., Tannenbaum, A., & Mischaikow, K. (2006). Coronary vessel trees from 3D imagery: a topological approach. Medical image analysis, 10(4), 548-59. doi:10.1016/j.media.2006.05.002

2012 Summer Project Week:RidgeExtractionAtrialWallSegmentation

2012-06-15T18:29:35Z

Anry:

__NOTOC__
<gallery>
Image:PW-MIT2012.png|[[2012_Summer_Project_Week#Projects|Projects List]]
Image:MRI1.jpg|Example of manual segmentation of the left atrial wall.
Image:Pic2.jpg|Polar transform of the left atrial wall.
Image:Pic3.jpg|Initial segmentation results.
</gallery>

==Background==
Catheter ablation has been proposed for treatment of atrial fibrillation arrhythmia. MRI data is used to explore lesion ablation and scarification locations are extracted from MRI image data. In addition, MRI analysis may help to predict if the ablation procedure will help a patient or not.
Many of these image analysis tasks are largely based on segmentation of left atrial wall, which is done manually or semi-automatically.
Automatic segmentation uses moving contours or surfaces (interfaces) to segment image data by minimizing a predefined energy function.
These moving interfaces are highly affected by image data, which can be thought as a force field pushing the interface to features of choice. Thus, the choice of interface attracting image features is critical.

==Key Investigators==
* BU: Arie Nakhmani
* BU and CCC UAB: Allen Tannenbaum

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are exploring different image features which are appropriate for an automatic segmentation. Our objective is to compare different topological extrema ranking algorithms (including maximum persistence [1,2] and perceptual ridge importance) to provide the most appropriate features, based on expert's manual segmentation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

* Implementing the comparison framework
* Ellipsoidal MRI transform
* Local ridge extraction
* Topological persistence algorithm implementation
* Comparison of different topological simplification approaches

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
We have developed topological simplification methods for removing noisy local maxima. The resulting ridge detection algorithm shows average error less then 2 pixels compared to the manual segmentation.

</div>
</div>

==References==
# Edelsbrunner, H., Harer, J., & Zomorodian, A. (2003). Hierarchical Morse--Smale Complexes for Piecewise Linear 2-Manifolds. Discrete and Computational Geometry, 30(1), 87-107. doi:10.1007/s00454-003-2926-5
# Szymczak, A., Stillman, A., Tannenbaum, A., & Mischaikow, K. (2006). Coronary vessel trees from 3D imagery: a topological approach. Medical image analysis, 10(4), 548-59. doi:10.1016/j.media.2006.05.002

File:Pic3.jpg

2012-06-15T18:27:16Z

Anry:

File:Pic2.jpg

2012-06-15T18:27:02Z

Anry:

2012 Summer Project Week:RidgeExtractionAtrialWallSegmentation

2012-06-08T00:58:55Z

Anry:

__NOTOC__
<gallery>
Image:PW-MIT2012.png|[[2012_Summer_Project_Week#Projects|Projects List]]
Image:MRI1.jpg|Example of manual segmentation of the left atrial wall.
</gallery>

==Background==
Catheter ablation has been proposed for treatment of atrial fibrillation arrhythmia. MRI data is used to explore lesion ablation and scarification locations are extracted from MRI image data. In addition, MRI analysis may help to predict if the ablation procedure will help a patient or not.
Many of these image analysis tasks are largely based on segmentation of left atrial wall, which is done manually or semi-automatically.
Automatic segmentation uses moving contours or surfaces (interfaces) to segment image data by minimizing a predefined energy function.
These moving interfaces are highly affected by image data, which can be thought as a force field pushing the interface to features of choice. Thus, the choice of interface attracting image features is critical.

==Key Investigators==
* BU: Arie Nakhmani
* BU and CCC UAB: Allen Tannenbaum

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are exploring different image features which are appropriate for an automatic segmentation. Our objective is to compare different topological extrema ranking algorithms (including maximum persistence [1,2] and perceptual ridge importance) to provide the most appropriate features, based on expert's manual segmentation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

* Implementing the comparison framework
* Ellipsoidal MRI transform
* Local ridge extraction
* Topological persistence algorithm implementation
* Comparison of different topological simplification approaches

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
We have developed topological simplification methods for removing noisy local maxima. The resulting ridge detection algorithm shows average error less then 2 pixels compared to the manual segmentation.

</div>
</div>

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as an Extension -- commandline

==References==
# Edelsbrunner, H., Harer, J., & Zomorodian, A. (2003). Hierarchical Morse--Smale Complexes for Piecewise Linear 2-Manifolds. Discrete and Computational Geometry, 30(1), 87-107. doi:10.1007/s00454-003-2926-5
# Szymczak, A., Stillman, A., Tannenbaum, A., & Mischaikow, K. (2006). Coronary vessel trees from 3D imagery: a topological approach. Medical image analysis, 10(4), 548-59. doi:10.1016/j.media.2006.05.002

2012 Summer Project Week:RidgeExtractionAtrialWallSegmentation

2012-06-08T00:49:32Z

Anry:

__NOTOC__
<!-- <gallery>
Image:PW-MIT2012.png|[[2012_Summer_Project_Week#Projects|Projects List]]
Image:MRI1.jpg|Example of manual segmentation of the left atrial wall.
</gallery>

==Background==
Catheter ablation has been proposed for treatment of atrial fibrillation arrhythmia. MRI data is used to explore lesion ablation and scarification locations are extracted from MRI image data. In addition, MRI analysis may help to predict if the ablation procedure will help a patient.
Many of these image analysis tasks are largely based on segmentation of left atrial wall, which is done manually or semi-automatically.
Automatic segmentation uses moving contours or surfaces (interfaces) to segment image data by minimizing a predefined energy function.
These moving interfaces are highly affected by image data, which can be thought as a force field pushing the interface to the features of choice. Thus, the choice of image features attracting the interface is critical.

==Key Investigators==
* BU: Arie Nakhmani
* BU and CCC UAB: Allen Tannenbaum

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are exploring different image features which are appropriate for automatic segmentation. Our objective is to compare different topological extrema ranking algorithms (including maximum persistence and perceptual ridge importance) to provide the most appropriate features, based on expert's manual segmentation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

* Implementing the comparison framework
* Ellipsoidal MRI transform
* Local ridge extraction
* Topological persistence algorithm implementation
* Comparison of different topological simplification approaches using the comparison framework

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
We have developed topological simplification methods for removing noisy local maxima. The resulting ridge detection algorithm shows average error less then 2 pixels compared to the manual segmentation.

</div>
</div>

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as an Extension -- commandline

==References==

File:MRI1.jpg

2012-06-08T00:46:10Z

Anry: