NAMIC Wiki - User contributions [en]

Iowa2014

2014-11-18T22:50:52Z

Ipek:

==Logistics==
{| border="00" cellpadding="8" cellspacing="0"
|-
| rowspan="2" align="left"|
*Date: November 18-19, 2014
*Location: Hardin Library for Health Sciences, Info Commons West, U. of Iowa, Iowa City, Iowa

[[Media:2014-Slicer-Iowa.pdf|Event flyer]]

This event is co-sponsored by [http://biomed-imaging.uiowa.edu/ the Iowa Institute for Biomedical Imaging], NIH [http://nac.spl.harvard.edu/ Neuroimage Analysis Center (NAC)] and [http://qiicr.og Quantitative Image Informatics for Cancer Research (QIICR)] projects.

The registration fee is $50. Space is limited, please register online at https://www.regonline.com/builder/site/Default.aspx?EventID=1632476

'''The registration deadline is November 11, 2014!'''
| style="background: #ebeced" colspan="2" align="center"|
|-
| style="background: #ebeced"| [[Image:2014-Slicer-Iowa.png|350px]]
|}

==Local Hosts==
* Milan Sonka, Ph.D., University of Iowa, IA
* Reinhard Beichel, Ph.D., University of Iowa, IA

Supporting personnel at Iowa:
* Ethan Ulrich
* Christian Bauer
* TBD from Hans' group
* Ipek Oguz

==Training Faculty==
* [http://www.spl.harvard.edu/~kikinis Ron Kikinis, M.D.], Surgical Planning Laboratory, Harvard Medical School, Boston, MA
* [http://www.spl.harvard.edu/~fedorov Andrey Fedorov, Ph.D.], Surgical Planning Laboratory, Harvard Medical School, Boston, MA
* [http://www.dclunie.com/ David Clunie], PixelMed Publishing

[[File:Iowa-Slicer-Workshop-2014-pic1.jpg|350px]]
[[File:Iowa-Slicer-Workshop-2014-pic2.jpg|350px]]
[[File:Iowa-Slicer-Workshop-2014-pic3.jpg|350px]]
[[File:Iowa-Slicer-Workshop-2014-pic4.jpg|350px]]
[[File:Iowa-Slicer-Workshop-2014-pic5.jpg|350px]]

== Agenda ==
{| border="1" cellpadding="5" width="1000"
|- style="background:#ebeced; color:#663300; font-size:120%" align="center"
| style="width:10%" | '''Time'''
| style="width:90%" colspan="2"| '''Tuesday, November 18, 2014'''
|-
| style="background:#efefef; color:#663300"| '''8:25 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Opening Remarks and Introduction (Milan Sonka, Reinhard Beichel)
|-
| style="background:#efefef; color:#663300"| '''8:30 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| [[media:|3D Slicer, a Platform for Medical Image Computing]] (Ron Kikinis)
|-
| style="background:#efefef; color:#663300"| '''9:00 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| [[media:3DDataLoadingandVisualization_SoniaPujol_slicer4.4.pdf‎ |‎ Hands-on Session 1: Data Loading and 3D Visualization (Sonia Pujol)]]
|-
| style="background:#efefef; color:#663300"| '''10:00 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Coffee-break
|-
| style="background:#efefef; color:#663300"| '''10:15 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Hands-on Session 2: Contributing extension to 3D Slicer - Part 1 (Andrey Fedorov) [http://goo.gl/IP4cdg slides]
|-
| style="background:#efefef; color:#663300"| '''11:15 PM'''
| style="background:#F5EBD6; color:#522200" colspan="2"| Lunch (will be provided)
|-
| style="background:#efefef; color:#663300"| '''12:15 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Hands-on Session 3: Contributing extension to 3D Slicer - Part 2 (Andrey Fedorov) [http://goo.gl/IP4cdg slides]
|-
| style="background:#efefef; color:#663300"| '''2:00 PM'''
| style="background:#F5EBD6; color:#522200" colspan="2"| Break
|-
| style="background:#efefef; color:#663300"| '''2:30 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| DICOM Support for Quantitative Imaging (David Clunie) [[media:20141118 Iowa Clunie DICOMSupportForQuantitativeImaging.pptx]]
|-
| style="background:#efefef; color:#663300"| '''3:30 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| QIICR: Support for advanced DICOM objects in 3D Slicer (Andrey Fedorov)
|-
| style="background:#FFFFFF; height:35px" colspan="3" |
|-style="background:#ebeced; color:#663300; font-size:120%" align="center"
| style="width:10%" | '''Time'''
| style="width:90%" colspan="2"| '''Wednesday, November 19, 2014'''
|-
| style="background:#efefef; color:#663300"| '''09:30-12:00 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| QIICR meetings (ad hoc)
|-
|}

==Datasets==

* [[media:3DVisualizationData.zip | Dataset1: 3DVisualizationDataset.zip]]

File:Iowa-Slicer-Workshop-2014-pic5.jpg

2014-11-18T22:50:40Z

Ipek:

Iowa2014

2014-11-18T21:00:18Z

Ipek:

==Logistics==
{| border="00" cellpadding="8" cellspacing="0"
|-
| rowspan="2" align="left"|
*Date: November 18-19, 2014
*Location: Hardin Library for Health Sciences, Info Commons West, U. of Iowa, Iowa City, Iowa

[[Media:2014-Slicer-Iowa.pdf|Event flyer]]

This event is co-sponsored by [http://biomed-imaging.uiowa.edu/ the Iowa Institute for Biomedical Imaging], NIH [http://nac.spl.harvard.edu/ Neuroimage Analysis Center (NAC)] and [http://qiicr.og Quantitative Image Informatics for Cancer Research (QIICR)] projects.

The registration fee is $50. Space is limited, please register online at https://www.regonline.com/builder/site/Default.aspx?EventID=1632476

'''The registration deadline is November 11, 2014!'''
| style="background: #ebeced" colspan="2" align="center"|
|-
| style="background: #ebeced"| [[Image:2014-Slicer-Iowa.png|350px]]
|}

==Local Hosts==
* Milan Sonka, Ph.D., University of Iowa, IA
* Reinhard Beichel, Ph.D., University of Iowa, IA

Supporting personnel at Iowa:
* Ethan Ulrich
* Christian Bauer
* TBD from Hans' group
* Ipek Oguz

==Training Faculty==
* [http://www.spl.harvard.edu/~kikinis Ron Kikinis, M.D.], Surgical Planning Laboratory, Harvard Medical School, Boston, MA
* [http://www.spl.harvard.edu/~fedorov Andrey Fedorov, Ph.D.], Surgical Planning Laboratory, Harvard Medical School, Boston, MA
* [http://www.dclunie.com/ David Clunie], PixelMed Publishing

[[File:Iowa-Slicer-Workshop-2014-pic1.jpg|350px]]
[[File:Iowa-Slicer-Workshop-2014-pic2.jpg|350px]]
[[File:Iowa-Slicer-Workshop-2014-pic3.jpg|350px]]
[[File:Iowa-Slicer-Workshop-2014-pic4.jpg|350px]]

== Agenda ==
{| border="1" cellpadding="5" width="1000"
|- style="background:#ebeced; color:#663300; font-size:120%" align="center"
| style="width:10%" | '''Time'''
| style="width:90%" colspan="2"| '''Tuesday, November 18, 2014'''
|-
| style="background:#efefef; color:#663300"| '''8:25 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Opening Remarks and Introduction (Milan Sonka, Reinhard Beichel)
|-
| style="background:#efefef; color:#663300"| '''8:30 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| [[media:|3D Slicer, a Platform for Medical Image Computing]] (Ron Kikinis)
|-
| style="background:#efefef; color:#663300"| '''9:00 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| [[media:3DDataLoadingandVisualization_SoniaPujol_slicer4.4.pdf‎ |‎ Hands-on Session 1: Data Loading and 3D Visualization (Sonia Pujol)]]
|-
| style="background:#efefef; color:#663300"| '''10:00 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Coffee-break
|-
| style="background:#efefef; color:#663300"| '''10:15 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Hands-on Session 2: Contributing extension to 3D Slicer - Part 1 (Andrey Fedorov) [http://goo.gl/IP4cdg slides]
|-
| style="background:#efefef; color:#663300"| '''11:15 PM'''
| style="background:#F5EBD6; color:#522200" colspan="2"| Lunch (will be provided)
|-
| style="background:#efefef; color:#663300"| '''12:15 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Hands-on Session 3: Contributing extension to 3D Slicer - Part 2 (Andrey Fedorov) [http://goo.gl/IP4cdg slides]
|-
| style="background:#efefef; color:#663300"| '''2:00 PM'''
| style="background:#F5EBD6; color:#522200" colspan="2"| Break
|-
| style="background:#efefef; color:#663300"| '''2:30 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| DICOM Support for Quantitative Imaging (David Clunie) [[media:20141118 Iowa Clunie DICOMSupportForQuantitativeImaging.pptx]]
|-
| style="background:#efefef; color:#663300"| '''3:30 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| QIICR: Support for advanced DICOM objects in 3D Slicer (Andrey Fedorov)
|-
| style="background:#FFFFFF; height:35px" colspan="3" |
|-style="background:#ebeced; color:#663300; font-size:120%" align="center"
| style="width:10%" | '''Time'''
| style="width:90%" colspan="2"| '''Wednesday, November 19, 2014'''
|-
| style="background:#efefef; color:#663300"| '''09:30-12:00 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| QIICR meetings (ad hoc)
|-
|}

==Datasets==

* [[media:3DVisualizationData.zip | Dataset1: 3DVisualizationDataset.zip]]

File:Iowa-Slicer-Workshop-2014-pic4.jpg

2014-11-18T20:59:59Z

Ipek:

Iowa2014

2014-11-18T15:43:51Z

Ipek:

==Logistics==
{| border="00" cellpadding="8" cellspacing="0"
|-
| rowspan="2" align="left"|
*Date: November 18-19, 2014
*Location: Hardin Library for Health Sciences, Info Commons West, U. of Iowa, Iowa City, Iowa

[[Media:2014-Slicer-Iowa.pdf|Event flyer]]

This event is co-sponsored by [http://biomed-imaging.uiowa.edu/ the Iowa Institute for Biomedical Imaging], NIH [http://nac.spl.harvard.edu/ Neuroimage Analysis Center (NAC)] and [http://qiicr.og Quantitative Image Informatics for Cancer Research (QIICR)] projects.

The registration fee is $50. Space is limited, please register online at https://www.regonline.com/builder/site/Default.aspx?EventID=1632476

'''The registration deadline is November 11, 2014!'''
| style="background: #ebeced" colspan="2" align="center"|
|-
| style="background: #ebeced"| [[Image:2014-Slicer-Iowa.png|350px]]
|}

==Local Hosts==
* Milan Sonka, Ph.D., University of Iowa, IA
* Reinhard Beichel, Ph.D., University of Iowa, IA

Supporting personnel at Iowa:
* Ethan Ulrich
* Christian Bauer
* TBD from Hans' group
* Ipek Oguz

==Training Faculty==
* [http://www.spl.harvard.edu/~kikinis Ron Kikinis, M.D.], Surgical Planning Laboratory, Harvard Medical School, Boston, MA
* [http://www.spl.harvard.edu/~fedorov Andrey Fedorov, Ph.D.], Surgical Planning Laboratory, Harvard Medical School, Boston, MA
* [http://www.dclunie.com/ David Clunie], PixelMed Publishing

[[File:Iowa-Slicer-Workshop-2014-pic1.jpg|350px]]
[[File:Iowa-Slicer-Workshop-2014-pic2.jpg|350px]]
[[File:Iowa-Slicer-Workshop-2014-pic3.jpg|350px]]

== Agenda ==
{| border="1" cellpadding="5" width="1000"
|- style="background:#ebeced; color:#663300; font-size:120%" align="center"
| style="width:10%" | '''Time'''
| style="width:90%" colspan="2"| '''Tuesday, November 18, 2014'''
|-
| style="background:#efefef; color:#663300"| '''8:25 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Opening Remarks and Introduction (Milan Sonka, Reinhard Beichel)
|-
| style="background:#efefef; color:#663300"| '''8:30 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| [[media:|3D Slicer, a Platform for Medical Image Computing]] (Ron Kikinis)
|-
| style="background:#efefef; color:#663300"| '''9:00 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| [[media:3DDataLoadingandVisualization_SoniaPujol_slicer4.4.pdf‎ |‎ Hands-on Session 1: Data Loading and 3D Visualization (Sonia Pujol)]]
|-
| style="background:#efefef; color:#663300"| '''10:00 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Coffee-break
|-
| style="background:#efefef; color:#663300"| '''10:15 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Hands-on Session 2: Contributing extension to 3D Slicer - Part 1 (Andrey Fedorov) [http://goo.gl/IP4cdg slides]
|-
| style="background:#efefef; color:#663300"| '''11:15 PM'''
| style="background:#F5EBD6; color:#522200" colspan="2"| Lunch (will be provided)
|-
| style="background:#efefef; color:#663300"| '''12:15 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Hands-on Session 3: Contributing extension to 3D Slicer - Part 2 (Andrey Fedorov) [http://goo.gl/IP4cdg slides]
|-
| style="background:#efefef; color:#663300"| '''2:00 PM'''
| style="background:#F5EBD6; color:#522200" colspan="2"| Break
|-
| style="background:#efefef; color:#663300"| '''2:30 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| DICOM Support for Quantitative Imaging (David Clunie)
|-
| style="background:#efefef; color:#663300"| '''3:30 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| QIICR: Support for advanced DICOM objects in 3D Slicer (Andrey Fedorov)
|-
| style="background:#FFFFFF; height:35px" colspan="3" |
|-style="background:#ebeced; color:#663300; font-size:120%" align="center"
| style="width:10%" | '''Time'''
| style="width:90%" colspan="2"| '''Wednesday, November 19, 2014'''
|-
| style="background:#efefef; color:#663300"| '''09:30-12:00 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| QIICR meetings (ad hoc)
|-
|}

==Datasets==

* [[media:3DVisualizationData.zip | Dataset1: 3DVisualizationDataset.zip]]

File:Iowa-Slicer-Workshop-2014-pic3.jpg

2014-11-18T15:42:16Z

Ipek: uploaded a new version of "File:Iowa-Slicer-Workshop-2014-pic3.jpg"

File:Iowa-Slicer-Workshop-2014-pic3.jpg

2014-11-18T15:41:38Z

Ipek:

File:Iowa-Slicer-Workshop-2014-pic2.jpg

2014-11-18T15:41:18Z

Ipek:

File:Iowa-Slicer-Workshop-2014-pic1.jpg

2014-11-18T15:41:00Z

Ipek:

Iowa2014

2014-10-15T18:35:55Z

Ipek: /* Local Hosts */

==Logistics==
{| border="00" cellpadding="8" cellspacing="0"
|-
| rowspan="2" align="left"|
*Date: November 18-19, 2014
*Location: Hardin Library for Health Sciences, Info Commons West, U. of Iowa, Iowa City, Iowa

[[Media:2014-Slicer-Iowa.pdf|Event flyer]]

This event is co-sponsored by [http://biomed-imaging.uiowa.edu/ the Iowa Institute for Biomedical Imaging], NIH [http://nac.spl.harvard.edu/ Neuroimage Analysis Center (NAC)] and [http://qiicr.og Quantitative Image Informatics for Cancer Research (QIICR)] projects.

The registration fee is $50. Space is limited, please register online at https://www.regonline.com/builder/site/Default.aspx?EventID=1632476

'''The registration deadline is November 11, 2014!'''
| style="background: #ebeced" colspan="2" align="center"|
|-
| style="background: #ebeced"| [[Image:2014-Slicer-Iowa.png|350px]]
|}

==Local Hosts==
* Milan Sonka, Ph.D., University of Iowa, IA
* Reinhard Beichel, Ph.D., University of Iowa, IA

Supporting personnel at Iowa:
* Ethan Ulrich
* Christian Bauer
* TBD from Hans' group
* Ipek Oguz

==Training Faculty==
* [http://www.spl.harvard.edu/~kikinis Ron Kikinis, M.D.], Surgical Planning Laboratory, Harvard Medical School, Boston, MA
* [http://www.spl.harvard.edu/~spujol Sonia Pujol, Ph.D.], Surgical Planning Laboratory, Harvard Medical School, Boston, MA
* [http://www.spl.harvard.edu/~fedorov Andrey Fedorov, Ph.D.], Surgical Planning Laboratory, Harvard Medical School, Boston, MA
* [http://www.dclunie.com/ David Clunie], PixelMed Publishing

== Agenda ==
{| border="1" cellpadding="5" width="1000"
|- style="background:#ebeced; color:#663300; font-size:120%" align="center"
| style="width:10%" | '''Time'''
| style="width:90%" colspan="2"| '''Tuesday, November 18, 2014'''
|-
| style="background:#efefef; color:#663300"| '''8:25 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Opening Remarks and Introduction (Milan Sonka, Reinhard Beichel)
|-
| style="background:#efefef; color:#663300"| '''8:30 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| [[media:2012-03-20-Iowa.pptx|3D Slicer, a Platform for Medical Image Computing]] (Ron Kikinis)
|-
| style="background:#efefef; color:#663300"| '''9:00 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| [[media:DataLoadingAndVisualizationSlicer4_SoniaPujol.pdf |‎ Data Loading and 3D Visualization (Sonia Pujol)]]
|-
| style="background:#efefef; color:#663300"| '''10:00 AM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Coffee-break
|-
| style="background:#efefef; color:#663300"| '''10:15 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Tutorial on Python programming in 3D Slicer
|-
| style="background:#efefef; color:#663300"| '''11:15 PM'''
| style="background:#F5EBD6; color:#522200" colspan="2"| Lunch (will be provided)
|-
| style="background:#efefef; color:#663300"| '''12:15 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| Hands-on session: the process of creating 3D Slicer extension (Andrey Fedorov)
|-
| style="background:#efefef; color:#663300"| '''2:00 PM'''
| style="background:#F5EBD6; color:#522200" colspan="2"| Break
|-
| style="background:#efefef; color:#663300"| '''2:15 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| DICOM Support for Quantitative Imaging (David Clunie)
|-
| style="background:#efefef; color:#663300"| '''3:15 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| QIICR: Support for advanced DICOM objects in 3D Slicer (Andrey Fedorov)
|-
| style="background:#FFFFFF; height:35px" colspan="3" |
|-style="background:#ebeced; color:#663300; font-size:120%" align="center"
| style="width:10%" | '''Time'''
| style="width:90%" colspan="2"| '''Wednesday, November 19, 2014'''
|-
| style="background:#efefef; color:#663300"| '''09:30-12:00 PM'''
| style="background:#FBF7BB; color:#522200" colspan="2"| QIICR meetings (ad hoc)
|-
|}

Talk:Whole-Brain-Tractography-Wizard

2012-01-12T00:46:04Z

Ipek: Created page with 'it'd be good to mention somewhere that you can do "slicer --designer" (for editing your widgets with designer) JC's advice for getting things to work: *Keep everything outside t…'

it'd be good to mention somewhere that you can do "slicer --designer" (for editing your widgets with designer)

JC's advice for getting things to work:
*Keep everything outside the slicer code - cleaner
*Also, we merged the two cmakelists (the top level and the Lib/CMakeLists.txt) to make things cleaner

*In the main .py file, import the Lib after def setup(self): - otherwise the path is not setup properly and slicer can't find our lib

*We had to comment out everything in the cmakelists.txt under # Generate extension description file '<EXTENSION_NAME>.s4ext' - jc says this is a bug on the slicer side that will hopefully be fixed soon

2012 Winter Project Week:ParticleWrapper

2011-12-15T21:09:37Z

Ipek: Created page with '__NOTOC__ <gallery> Image:PW-SLC2012.png|Projects List </gallery> ==Key Investigators== * UNC: Ipek Oguz, Beatriz Paniagua, Martin Styner *…'

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* UNC: Ipek Oguz, Beatriz Paniagua, Martin Styner
* Utah SCI: Josh Cates, Manasi Datar, Ross Whitaker

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

<h3>Objective</h3>
The UNC and Utah groups have created many tools around ShapeWorks over the years. While this is great for ourselves for development and debugging and such, it is a bit of a nightmare for an outside user to learn. We'd like to create an easy-to-use wrapper Slicer module that will present a unified front. The idea is to start with just a set of binary segmentations, go through all the different preprocessing steps such as anti-aliasing, distance transform, etc, run correspondence, and do postprocessing which involves creating new surface meshes that are in correspondence as the final output. All the intermediate steps will be automatically run.

</div>

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

<h3>Approach, Plan</h3>

All the necessary pieces of software already exist and have been thoroughly tested on many datasets, so we just need to pull the pieces together.
We essentially need to figure out how to keep the interface simple while keeping the tools flexible enough. This will probably mean separating the necessary inputs from all the advanced settings, and providing sensible defaults for all of those.

</div>

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

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered in NITRC, as an extension to the current ShapeWorks pipeline

==References==

</div>

2012 Winter Project Week

2011-12-15T20:58:06Z

Ipek: /* Shape Analysis */

Back to [[Project Events]], [[Events]]
Back to [[Project Events]], [[AHM_2012]], [[Events]]

__NOTOC__
[[image:PW-SLC2012.png|300px]]

== Dates.Venue.Registration ==

Please [[AHM_2012#Dates_Venue_Registration|click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2012#Agenda|click here for the agenda for AHM 2012 and Project Week]].

==Background==

From January 9-13, 2012, the 14th project week for hands-on research and development activity in Neuroscience and Image-Guided Therapy applications will be hosted in Salt Lake City, Utah. Participant engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([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], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions.

Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project.

On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.

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

==Projects==

===Traumatic Brain Injury ===

* [[2012_Winter_Project_Week:TBIClinicalAnalysis|Segmentation of Serial MRI of TBI patients
using Personalized Atlas Construction]] (Bo Wang, Marcel Prastawa, Andrei Irimia, Micah Chambers, Jack van Horn, Guido Gerig, Danielle Pace, Stephen Aylward)
* [[2012_Winter_Project_Week:TBIDTIAnalysis|Registration and analysis of white matter tract changes in TBI]] (Clement Vachet, Anuja Sharma, Marcel Prastawa, Andrei Irimia, Jack van Horn, Guido Gerig, Martin Styner, Danielle Pace, Stephen Aylward)
* [[2012_Winter_Project_Week:TBIValidation|Validation, visualization and analysis of segmentation for TBI]] (Bo Wang, Marcel Prastawa, Andrei Irimia, Micah Chambers, Jack van Horn, Guido Gerig, Danielle Pace, Stephen Aylward)
*Geometric Metamorphosis for TBI (Danielle Pace, Marc Niethammer, Marcel Prastawa, Andrei Irimia, Jack van Horn, Danielle Pace, Stephen Aylward)
* [[2012_Winter_Project_Week:TBIRegistration|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] (Yifei Lou, Andrei Irimia, Patricio Vela, Allen Tannenbaum, Micah C. Chambers, Jack Van Horn and Paul M. Vespa, Danielle Pace, Stephen Aylward)
* [[2012_Winter_Project_Week:TBIRegistration|Integration of unscented Kalman filter (UKF) based multi-tensor tractography in Slicer]] (Christian Baumgartner, Yogesh Rathi, Carl-Fredrik Westin)

===Predict Huntington's Disease===
* [[2012_Winter_Project_Week:SPIEWorkshop|SPIE DTI Workshop Preparation: Perform DTI Quality Control]] (Jean-Baptiste Berger, Sonia Pujol, Guido Gerig, Clement Vachet, Martin Styner)
* [[2012_Winter_Project_Week:DWIPhantom|DTI tractography phantom: a software for evaluating tractography algorithms]] (Gwendoline Roger,Yundi Shi, Clement Vachet, Martin Styner, Sylvain Gouttard)
* [[2012_Winter_Project_Week:FVLight|FiberViewerLight: a fiber bundle visualization and clustering tool]] (Jean-Baptiste Berger, Clement Vachet, Martin Styner)
* [[2012_Winter_Project_Week:DTIAFA|DTIAtlasFiberAnalyzer]] (Jean-Baptiste Berger, Yundi Shi, Clement Vachet, Martin Styner)
* [[2012_Winter_Project_Week:PairWiseDTIRegistration|Pairwise DTI registration: DTI-Reg]] (Clement Vachet, Hans Johnson, Martin Styner)
* [[2012_Winter_Project_Week:ShapeAnalysisSubcorticalStructuresHD|Morphometric analysis in subcortical structures in HD]] (Beatriz Paniagua, Clement Vachet, Hans Johnson, Martin Styner)
* [[2012_Winter_Project_Week:DTI pipeline|Applying our DTI pipeline to analyse HD data]] (Gopalkrishna Veni, Hans Johnson, Martin Styner, Ross Whitaker)
* [[2012_Winter_Project_Week: DTI Change Modeling | Longitudinal change modeling of fiber tracts in serial HD DTI data]] (Anuja Sharma, Hans Johnson, Guido Gerig)
* [[2012_Winter_Project_Week: Continuous 4D shapes | Continuous 4d shape models from time-discrete data: Subcortical structures in HD]] (James Fishbaugh, Hans Johnson, Guido Gerig)

===Atrial fibrillation ===
* [[2012_Winter_Project_Week:EndoSeg|Endocardial Segmentation in DE-MRI for AFib]] (Yi Gao, Liang-Jia Zhu, Josh Cates, Greg Gardner, Alan Morris, Danny Perry, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
* [[2012_Winter_Project_Week:LAWallRegistration|Longitudinal Alignment and Visualization of Left-Atrial Wall from DEMRI and MRA]] (Josh Cates, Yi Gao, Liang-Jia Zhu, Greg Gardner, Alan Morris, Danny Perry, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
* [[2012_Winter_Project_Week:PVRegistration|Longitudinal Alignment and Visualization of Pulmonary Veins from DEMRI and MRA]] (Josh Cates, Yi Gao, Liang-Jia Zhu, Greg Gardner, Alan Morris, Danny Perry, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
* [[2012_Winter_Project_Week:RealTime|OpenIGT for realtime MRI-guided RF ablation]] (Gene Payne, Rob MacLeod, and Junichi Tokuda)

===Head and Neck Cancer ===
* A patch-based approach to the segmentation of organs of risk (Christian Wachinger, Polina Golland)
* RT dose comparison tool for Slicer (Nadya Shusharina, Greg Sharp)
* [[2012_Winter_Project_Week:InteractiveSegmentation|Interactive editing tools for segmentation]] (Greg Sharp, Steve Pieper)

===IGT for Surgery and Radiation Treatments===
*[[2012_Winter_Project_Week:PelvicRegistration|Deformable prostate registration: 3D ultrasound to MRI]] (Mehdi Moradi, Jan Egger, Andrey Fedorov)
*iGyne (Jan Egger, Xiaojun Chen, Radhika Tibrewal, Mehdi Moradi)
*[[2012_Winter_Project_Week:OpenIGTLink_Interface_for_Slicer4| OpenIGTLink interface for Slicer4]] (Junichi Tokuda, Clif Burdette/Jack Blevins, Tamas Ungi, Andras Lasso)
*[[2012_Winter_Project_Week:Needle Detection in MR Images for Brachytherapy in AMIGO|Needle Detection in MR Images for Brahytherapy in AMIGO]] (Radhika Tibrewal, Jan Egger, Xiaojun Chen)
*[[2012_Winter_Project_Week:LiveUltrasound|Live ultrasound in Slicer4 using Plus and OpenIGTLink]] (Tamas Ungi, Elvis Chen)
*[[2012_Winter_Project_Week:OsteoPlan|Surgical Planning for facial osteotomy (OsteoPlan)]] (Laurent, Noby)
*Generating a hybrid MR Spectroscopic Dataset under Slicer (Isaiah Norton, Jan Egger, Tina Kapur)
* [[2012_Winter_Project_Week:RTTools|RT tools for Slicer4]] (Csaba Pinter, Kevin Wang, Andras Lasso, Greg Sharp)
* [[2012_Winter_Project_Week:RTSS|RT structure set data representation]] (Greg Sharp, Andras Lasso, Steve Pieper, etc.)

===Musculoskeletal System===
* [[2012_Winter_Project_Week:Radnostics|Spine Segmentation & Osteoporosis Screening In CT Imaging Studies]] (Anthony Blumfield)

===Registration===
* [[2012_Winter_Project_Week:CMFreg|Framework for Cranio-Maxillo Facial registration in Slicer3]] (Beatriz Paniagua, Lucia Cevidanes, Martin Styner)
* [[2012_Winter_Project_Week:SlidingOrgans|Registration in the presence of sliding between organs (Danielle Pace, Marc Neithammer, Stephen Aylward)]]
* [[2012_Winter_Project_Week:GeometricMetamorphosis|Estimating the infiltration / recession of pathologies independent of background deformations (Danielle Pace, Stephen Aylward, Marc Niethammer)]]

===Shape Analysis===
* [[2012_Winter_Project_Week:PNSnormals|Principal Nested Spheres Normal Consistency in ShapeWorks]] (Beatriz Paniagua, Josh Cates, Manasi Datar, Ross Whitaker, Martin Styner)
* [[2012_Winter_Project_Week:GeomIndicesSlicer4|Porting of White Matter Geometric Indices Module to Slicer4]] (Peter Savadjiev)
* [[2012_Winter_Project_Week:ParticleWrapper|Slicer end-to-end particle correspondence wrapper module]] (Ipek Oguz, Beatriz Paniagua, Josh Cates, Manasi Datar, Ross Whitaker, Martin Styner)

===NA-MIC Kit Internals===
*Slicer4 release (Jean-Christophe Fillion-Robin (JC), and Julien Finet (J2))
*Slicer4 extensions (JC)
*Slicer4 documentation (JC)
*Slicer4 GUI Testing (Benjamin Long, J2, JC)
*Slicer4 data on MIDAS (Josh Cates, Patrick Reynolds)
*Slicer4 Scene Views Module (Nicole Aucoin)
*Slicer4 Annotations Module
** File format refactor (Nicole Aucoin)
** QT 3D Text rendering proof of concept (Julien Finet, Steve Pieper, Nicole Aucoin)
*[[2012_Project_Week:DICOM|DICOM Networking, Database, and Slicer Integration]] (Steve, Andrey, Andras)
*[[2012_Project_Week:EditorExtensions|Editor Extension Examples and Debugging]] (Steve, Andrey, Jc, Hans, Satra)
*[[2012_Project_Week:ViewerControls|Redesign of the slice viewer control panels]] (Julien Finet, Ron Kikinis, Hans Johnson, Greg Sharp)
* [[2012_Project_Week:AutomatedTesting |Automated Testing (Sonia Pujol, Steve Pieper, Jc, Benjamin)]]
* Remove legacy code from slicer4 (itk, modules, build scripts) (Hans, Jim, Steve, J2, JC)
*[[2012_Project_Week:BatchProcessing|Batch Processing with Slicer Modules]] (Steve, Andrey, JC, Hans, Satra)
*[[2012_Project_Week:4DImageSlicer4|Support for 4D Images in Slicer4]] (Andrey, Steve, Junichi, Alex)
* AIM, DICOM SR and Slicer annotations (Andrey, Steve, Nicole, Jayashree)

=== Preparation ===

#Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]
#Starting Thursday, October 27th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 27: MGH DBP
#*November 3: Iowa DBP Huntingtons, Engineering Infrastructure Topics
#*November 10: Utah Atrial Fibrillation DBP
#*November 17: UCLA TBI DBP
#*November 24: No call. thanksgiving.
#*December 1:
#*December 8:
#*December 15:Finalize Projects
#*January 5: Loose Ends
#By December 15: [[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 December 15: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
##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. MIDAS, xNAT). 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.)
##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. (Zack)
#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...

File:Oguz-DTIApps-SfN11.pdf

2011-11-11T03:04:09Z

Ipek:

SFN2011 Diffusion Tensor Imaging Analysis Workshop

2011-11-11T02:54:02Z

Ipek: /* Tentative Agenda */

{| class="wikitable" border="1" cellpadding="8" cellspacing="1"
| colspan="3" style="width:450px" |[[Image:Am2011_header.jpg]]
|-
| style="width:150px" |[[Image:NAMIC.jpg‎]]
| style="width:150px" |[[Image:Logo_nac.gif‎]]
| style="width:150px" |[[Image:NCIGTlogo.gif]]
|}
[[File:NA-MIC_DTI_workshop_SfN2011_flyer.png | 600 px | right]]
==White Matter Exploration with Diffusion Tensor Imaging: Fundamentals and Perspectives - A Hands-On Workshop by the National Alliance for Medical Imaging Computing (NA-MIC)==
Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) provides a non-invasive window on the complex organization of the brain white matter in-vivo. The technique offers tremendous opportunities for gaining insights into the architecture of major white matter pathways, and DT-MRI findings have the potential to play a critical role in the extraction of meaningful information for diagnosis, prognosis and following of treatment response. The workshop will guide participants through the fundamentals of the acquisition, analysis and interpretation of DT-MRI data. The format will alternate a series of lectures and hands-on sessions with the participants running DT-MRI analysis on their own laptops, to provide a conceptual understanding of the underlying theory of Diffusion Imaging, and a practical experience of extracting relevant clinical information from DT-MRI images. The hands-on sessions will use the 3DSlicer software, an open-source software package for medical image analysis and 3D visualization used in biomedical and clinical research worldwide. The workshop will educate the participants on the fundamentals of DT-MRI data required to make decisions about how to analyze their own data for neuroscience studies.

==Introduction==
This workshop is a satellite event of the [http://www.sfn.org/am2011/ Annual Meeting of the Society for Neuroscience], November 12-16, Washington, DC

= Satellite Workshop Goals =
* To provide tutorials on DTI data validation and management (including how to put your image data into NRRD format).<br />
* To provide hands on tutorials in the use of NA-MIC DTI analysis tools.<br />
* To establish contact between NA-MIC Tookit experts (users and developers) and basic and clinical researchers in the global neuroimaging community.<br />

== Workshop Faculty ==

* [[User:SPujol|Sonia Pujol, PhD]], Harvard Medical School, Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital
* [[User:Randy|Randy Gollub, MD, PhD]], Harvard Medical School, Department of Psychiatry and Martinos Center, Department of Radiology, Massachusetts General Hospital
* [http://www.nmr.mgh.harvard.edu/martinos/people/showPerson.php?people_id=572 Anastasia Yendiki, PhD], Harvard Medical School, Martinos Center, Department of Radiology, Massachussets General Hospital
* [http://www.med.unc.edu/psych/directories/faculty/ipek-oguz-phd/ Ipek Oguz, PhD], University of North Carolina Chapel Hill

== '''Tentative Agenda''' ==
* 9:00 - 9:10 AM '''Goals of Workshop''' (Randy Gollub/Sonia Pujol)
* 9:10 - 10:10 AM '''Theoretical foundations of Diffusion Imaging''' (Anastasia Yendiki)
* 10:10 - 10:45 AM '''Getting to Know DTI really, really well- tensors, glyphs and more''' (Sonia Pujol)
* 10:45 -11:00 Coffee Break
* 11:00 - 12:00 PM [[Media:Oguz-DTIApps-SfN11.pdf | '''DTI in research and in the clinics: current uses and future roadmap''' ]](Ipek Oguz)
* 12:00 - 1:30 Lunch on own
* 1:30 - 2:00 '''Challenges in clinical transfer of DT-MRI: towards validation of tractography''' (Sonia Pujol)
* 2:00 - 3:00 '''Hands-on Session 1- Diffusion Tensor Imaging Analysis within 3DSlicer: from DWI images to 3D fiber bundles''' (Sonia Pujol/Randy Gollub/Anastasia Yendiki/Ipek Oguz)
* 3:15 - 3:30 Coffee Break
* 3:30 - 4:45 '''Hands-on Session 2- White Matter Exploration for Neurosurgical Planning''' (Sonia Pujol/Randy Gollub/Anastasia Yendiki/Ipek Oguz)
* 4:45 - 5:15 [http://www.birncommunity.org/resources/best-practices '''Lessons in Multi-site DTI Acquisition- the BIRN experience''' (Randy Gollub)]
* 5:15 - 5:30 Concluding remarks and Discussion
<br />

==Logistics and Registration==
* The workshop will be held on Friday November 11 from 9:00 am to 5:30 pm in '''Room 159A''' of the Walter E. Washington Convention Center, 801 Mount Vernon Place, NW, Washington.
* '''<span style="background-color: yellow"> Registration Fee: $50. Please register [http://www.cvent.com/d/xcq82y/4W here]. Only credit card payments are accepted.</span>'''
* For questions related to the workshop and to pre-register to the event, send an e-mail to Sonia Pujol (spujol at bwh.harvard.edu). Please include your contact information including full spelling of your name, degree, current institution, preferred email address for communication, your educational background and training and your reason for attending. We would also appreciate it if you would indicate the characteristics of the computer you will be using for the workshop (OS, RAM, Processor).

== Preparation for Workshop -- ''Important Information for all attendees'' ==

<span style="background-color: yellow">'''This is hands-on training. All participants must come with their own computer and install the software and data at the links below. Windows XP, Linux or MacOS 10.4 or greater computers are supported. A minimum of 1 GB of RAM (2 GB if possible) and a dedicated graphic accelerator with 64mb of on board graphic memory are required.'''</span><br />

*Software installation: Please install the Slicer3.6.3 release version appropriate to the laptop computer you'll be bringing to the workshop
* Windows: [http://www.na-mic.org/Slicer/Download/Release/win32/Slicer3-3.6.3-2011-03-04-win32.exe Slicer3-3.6.3-2011-03-04-win32.exe]
* Linux 64: [http://www.na-mic.org/Slicer/Download/Release/linux-x86_64/Slicer3-3.6.3-2011-03-04-linux-x86_64.tar.gz Slicer3-3.6.3-2011-03-04-linux-x86_64.tar.gz]
* Linux 32: [http://www.na-mic.org/Slicer/Download/Release/linux-x86/Slicer3-3.6.3-2011-03-04-linux-x86.tar.gz Slicer3-3.6.3-2011-03-04-linux-x86.tar.gz ]
* Mac Darwin: [http://www.na-mic.org/Slicer/Download/Release/darwin-x86/Slicer3-3.6.3-2011-03-04-darwin-x86.tar.gz Slicer3-3.6.3-2011-03-04-darwin-x86.tar.gz ]

*Datasets: Please download the [[media:DiffusionDataset.zip| Diffusion dataset]], and [[media:WhiteMatterExplorationData.zip‎| White Matter Exploration dataset]] in preparation for the workshop.

Suggested background reading for workshop:<br />

* Le Bihan D, Mangin JF, Poupon C, et al. Diffusion tensor imaging:. concepts and applications. J Magn Reson Imaging 2001;13:534–546 [http://www.meteoreservice.com/PDFs/JMRI_DTI.PDF [pdf download]]
* Westin CF, Maier SE, Mamata H, Nabavi A, Jolesz FA, Kikinis R., Processing and Visualization for Diffusion Tensor MRI. Medical Image Analysis, 6(2)::93-108, June 2002 [http://lmi.bwh.harvard.edu/papers/papers/westinMEDIA02.html [pdf dowload]]
* S. Mori and P.C.M. van Zijl, Fiber tracking: principles and strategies – a technical review. NMR in Biomedicine, 15(7-8):468-480, 2002 [[Media:Mori-intro-2002.pdf| [pdf download]]]<br /><br />
* Mukherjee P, Berman JI, Chung SW, Hess CP, Henry RG. Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings. AJNR American journal of neuroradiology (2008) vol. 29 (4) pp. 632-41
* Mukherjee P, Chung SW, Berman JI, Hess CP, Henry RG. Diffusion tensor MR imaging and fiber tractography: technical considerations. AJNR American journal of neuroradiology (2008) vol. 29 (5) pp. 843-52
* Diffusion MRI: Theory, Methods, and Applications. Edited by Derek K. Jones. Oxford University Press, 2011.

==To learn more about the NA-MIC Toolkit==
Please visit our [[Main_Page|Wiki page]] for general information and [[NA-MIC-Kit|the NA-MIC Kit pages]] for specific information.

== Slicer3 Training Survey ==

[http://www.surveymonkey.com/s/GZDXKXQ Click here to take the Slicer3 Training Survey]

==Slicer Community ==
Participants are invited to join the [http://www.slicer.org/pages/Mailinglist Slicer user and Slicer developer community] prior to the workshop, for questions and feature requests related to the software.

Projects:DiffusionGraphBasedConnectivity

2011-10-17T17:33:37Z

Ipek: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Muti-directional graph propagation for Diffusion Imaging based Connectivity =

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.

= Description =

[[Image:UNC_GraphbasedConnectivity_Ex1.png|thumb|300px|Slicer visualization of genu connectivity map overlaid on streamline genu tracts]]
Regional connectivity measurements derived from diffusion imaging datasets are of considerable interest in the neuroimaging community for better understanding white matter connectivity. Current connectivity measurements are usually either based on fiber tractography applied in a Monte-Carlo fashion or are variations of the Hamilton-Jacobi approach.

[[Image:UNC_GraphbasedConnectivity_Monkeycombined.png|thumb|300px|Average cost map computed on the rhesus monkey DWI atlas with the source located in the internal capsule (left) and the genu of the corpus callosum (right). The grayscale overlays show the FA map, and the 3D visualizations include the streamline tractography results from the same seed regions for comparison purposes.]]

We propose a novel, graph-based algorithm that provides a fully deterministic, efficient and stable connectivity measure. This method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation algorithm applied to sampled orientation distribution functions computed directly from the original diffusion imaging data. A maximum probability and a minimum cost approach are both possible.

So far we have results on very simple synthetic and limited real datasets to illustrate the potential of our method towards subject-specific connectivity measurements performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for studies of neuropathology.

We are currently working on the application of our algorithm to a publicly available synthetic dataset generated via Numerical Fiber Generator (NFG) (Close et al. A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms. NeuroImage (2009) vol. 47 (4) pp. 1288-300). This will allow us to thoroughly evaluate the algorithm as well as compare it to various existing connectivity/tractography methods.

[[Image:UNC_connectivity_nfg.png|thumb|500px|Application to NFG synthetic data. We start with a complex synthetic dataset, and we compute connectivity maps for each ROI defined by the start/end regions of each fiber bundle. These individual cost maps are compiled into a connectivity matrix that represents the connectivity values between each pair of ROI's. The ROI pairs along the diagonal (marked with a white dot) are the true connections (as given by the ground truth) in the data.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADiffusionGraphBasedConnectivity NA-MIC Publication Database on Graph based connectivity]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Wenyu Lu, Alexis Boucharin, Yundi Shi, Martin Styner

= Links =

[[Category: Diffusion MRI]]

Projects:DiffusionGraphBasedConnectivity

2011-10-17T17:33:08Z

Ipek:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Muti-directional graph propagation for Diffusion Imaging based Connectivity =

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.

= Description =

[[Image:UNC_GraphbasedConnectivity_Ex1.png|thumb|300px|Slicer visualization of genu connectivity map overlaid on streamline genu tracts]]
Regional connectivity measurements derived from diffusion imaging datasets are of considerable interest in the neuroimaging community for better understanding white matter connectivity. Current connectivity measurements are usually either based on fiber tractography applied in a Monte-Carlo fashion or are variations of the Hamilton-Jacobi approach.

[[Image:UNC_GraphbasedConnectivity_Monkeycombined.png|thumb|300px|Average cost map computed on the rhesus monkey DWI atlas with the source located in the internal capsule (left) and the genu of the corpus callosum (right). The grayscale overlays show the FA map, and the 3D visualizations include the streamline tractography results from the same seed regions for comparison purposes.]]

We propose a novel, graph-based algorithm that provides a fully deterministic, efficient and stable connectivity measure. This method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation algorithm applied to sampled orientation distribution functions computed directly from the original diffusion imaging data. A maximum probability and a minimum cost approach are both possible.

So far we have results on very simple synthetic and limited real datasets to illustrate the potential of our method towards subject-specific connectivity measurements performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for studies of neuropathology.

We are currently working on the application of our algorithm to a publicly available synthetic dataset generated via Numerical Fiber Generator (NFG) (Close et al. A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms. NeuroImage (2009) vol. 47 (4) pp. 1288-300). This will allow us to thoroughly evaluate the algorithm as well as compare it to various existing connectivity/tractography methods.

[[Image:UNC_connectivity_nfg.png|thumb|300px|Application to NFG synthetic data. We start with a complex synthetic dataset, and we compute connectivity maps for each ROI defined by the start/end regions of each fiber bundle. These individual cost maps are compiled into a connectivity matrix that represents the connectivity values between each pair of ROI's. The ROI pairs along the diagonal (marked with a white dot) are the true connections (as given by the ground truth) in the data.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADiffusionGraphBasedConnectivity NA-MIC Publication Database on Graph based connectivity]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Wenyu Lu, Alexis Boucharin, Yundi Shi, Martin Styner

= Links =

[[Category: Diffusion MRI]]

Projects:DiffusionGraphBasedConnectivity

2011-10-17T17:32:27Z

Ipek: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Muti-directional graph propagation for Diffusion Imaging based Connectivity =

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.

= Description =

[[Image:UNC_GraphbasedConnectivity_Ex1.png|thumb|300px|Slicer visualization of genu connectivity map overlaid on streamline genu tracts]]
Regional connectivity measurements derived from diffusion imaging datasets are of considerable interest in the neuroimaging community for better understanding white matter connectivity. Current connectivity measurements are usually either based on fiber tractography applied in a Monte-Carlo fashion or are variations of the Hamilton-Jacobi approach.

We propose a novel, graph-based algorithm that provides a fully deterministic, efficient and stable connectivity measure. This method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation algorithm applied to sampled orientation distribution functions computed directly from the original diffusion imaging data. A maximum probability and a minimum cost approach are both possible.

So far we have results on very simple synthetic and limited real datasets to illustrate the potential of our method towards subject-specific connectivity measurements performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for studies of neuropathology.

We are currently working on the application of our algorithm to a publicly available synthetic dataset generated via Numerical Fiber Generator (NFG) (Close et al. A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms. NeuroImage (2009) vol. 47 (4) pp. 1288-300). This will allow us to thoroughly evaluate the algorithm as well as compare it to various existing connectivity/tractography methods.

[[Image:UNC_GraphbasedConnectivity_Monkeycombined.png|thumb|300px|Average cost map computed on the rhesus monkey DWI atlas with the source located in the internal capsule (left) and the genu of the corpus callosum (right). The grayscale overlays show the FA map, and the 3D visualizations include the streamline tractography results from the same seed regions for comparison purposes.]]

[[Image:UNC_connectivity_nfg.png|thumb|300px|Application to NFG synthetic data. We start with a complex synthetic dataset, and we compute connectivity maps for each ROI defined by the start/end regions of each fiber bundle. These individual cost maps are compiled into a connectivity matrix that represents the connectivity values between each pair of ROI's. The ROI pairs along the diagonal (marked with a white dot) are the true connections (as given by the ground truth) in the data.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADiffusionGraphBasedConnectivity NA-MIC Publication Database on Graph based connectivity]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Wenyu Lu, Alexis Boucharin, Yundi Shi, Martin Styner

= Links =

[[Category: Diffusion MRI]]

Projects:DiffusionGraphBasedConnectivity

2011-10-17T17:31:46Z

Ipek: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Muti-directional graph propagation for Diffusion Imaging based Connectivity =

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.

= Description =

[[Image:UNC_GraphbasedConnectivity_Ex1.png|thumb|300px|Slicer visualization of genu connectivity map overlaid on streamline genu tracts]]
Regional connectivity measurements derived from diffusion imaging datasets are of considerable interest in the neuroimaging community for better understanding white matter connectivity. Current connectivity measurements are usually either based on fiber tractography applied in a Monte-Carlo fashion or are variations of the Hamilton-Jacobi approach.

We propose a novel, graph-based algorithm that provides a fully deterministic, efficient and stable connectivity measure. This method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation algorithm applied to sampled orientation distribution functions computed directly from the original diffusion imaging data. A maximum probability and a minimum cost approach are both possible.

So far we have results on very simple synthetic and limited real datasets to illustrate the potential of our method towards subject-specific connectivity measurements performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for studies of neuropathology.

We are currently working on the application of our algorithm to a publicly available synthetic dataset generated via Numerical Fiber Generator (NFG) (Close et al. A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms. NeuroImage (2009) vol. 47 (4) pp. 1288-300). This will allow us to thoroughly evaluate the algorithm as well as compare it to various existing connectivity/tractography methods.

[[Image:UNC_GraphbasedConnectivity_Monkeycombined.png|thumb|300px|Average cost map computed on the rhesus monkey DWI atlas with the source located in the internal capsule (left) and the genu of the corpus callosum (right). The grayscale overlays show the FA map, and the 3D visualizations include the streamline tractography results from the same seed regions for comparison purposes.]]

[[Image:UNC_connectivity_nfg.png|thumb|800px|Application to NFG synthetic data. We start with a complex synthetic dataset, and we compute connectivity maps for each ROI defined by the start/end regions of each fiber bundle. These individual cost maps are compiled into a connectivity matrix that represents the connectivity values between each pair of ROI's. The ROI pairs along the diagonal (marked with a white dot) are the true connections (as given by the ground truth) in the data.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADiffusionGraphBasedConnectivity NA-MIC Publication Database on Graph based connectivity]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Wenyu Lu, Alexis Boucharin, Yundi Shi, Martin Styner

= Links =

[[Category: Diffusion MRI]]

Projects:DiffusionGraphBasedConnectivity

2011-10-17T17:31:26Z

Ipek: /* Key Investigators */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Muti-directional graph propagation for Diffusion Imaging based Connectivity =

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.

= Description =

[[Image:UNC_GraphbasedConnectivity_Ex1.png|thumb|300px|Slicer visualization of genu connectivity map overlaid on streamline genu tracts]]
Regional connectivity measurements derived from diffusion imaging datasets are of considerable interest in the neuroimaging community for better understanding white matter connectivity. Current connectivity measurements are usually either based on fiber tractography applied in a Monte-Carlo fashion or are variations of the Hamilton-Jacobi approach.

We propose a novel, graph-based algorithm that provides a fully deterministic, efficient and stable connectivity measure. This method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation algorithm applied to sampled orientation distribution functions computed directly from the original diffusion imaging data. A maximum probability and a minimum cost approach are both possible.

So far we have results on very simple synthetic and limited real datasets to illustrate the potential of our method towards subject-specific connectivity measurements performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for studies of neuropathology.

We are currently working on the application of our algorithm to a publicly available synthetic dataset generated via Numerical Fiber Generator (NFG) (Close et al. A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms. NeuroImage (2009) vol. 47 (4) pp. 1288-300). This will allow us to thoroughly evaluate the algorithm as well as compare it to various existing connectivity/tractography methods.

[[Image:UNC_GraphbasedConnectivity_Monkeycombined.png|thumb|300px|Average cost map computed on the rhesus monkey DWI atlas with the source located in the internal capsule (left) and the genu of the corpus callosum (right). The grayscale overlays show the FA map, and the 3D visualizations include the streamline tractography results from the same seed regions for comparison purposes.]]

[[Image:UNC_connectivity_nfg.png|thumb|1000px|Application to NFG synthetic data. We start with a complex synthetic dataset, and we compute connectivity maps for each ROI defined by the start/end regions of each fiber bundle. These individual cost maps are compiled into a connectivity matrix that represents the connectivity values between each pair of ROI's. The ROI pairs along the diagonal (marked with a white dot) are the true connections (as given by the ground truth) in the data.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADiffusionGraphBasedConnectivity NA-MIC Publication Database on Graph based connectivity]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Wenyu Lu, Alexis Boucharin, Yundi Shi, Martin Styner

= Links =

[[Category: Diffusion MRI]]

Projects:DiffusionGraphBasedConnectivity

2011-10-17T17:30:35Z

Ipek: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Muti-directional graph propagation for Diffusion Imaging based Connectivity =

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.

= Description =

[[Image:UNC_GraphbasedConnectivity_Ex1.png|thumb|300px|Slicer visualization of genu connectivity map overlaid on streamline genu tracts]]
Regional connectivity measurements derived from diffusion imaging datasets are of considerable interest in the neuroimaging community for better understanding white matter connectivity. Current connectivity measurements are usually either based on fiber tractography applied in a Monte-Carlo fashion or are variations of the Hamilton-Jacobi approach.

We propose a novel, graph-based algorithm that provides a fully deterministic, efficient and stable connectivity measure. This method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation algorithm applied to sampled orientation distribution functions computed directly from the original diffusion imaging data. A maximum probability and a minimum cost approach are both possible.

So far we have results on very simple synthetic and limited real datasets to illustrate the potential of our method towards subject-specific connectivity measurements performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for studies of neuropathology.

We are currently working on the application of our algorithm to a publicly available synthetic dataset generated via Numerical Fiber Generator (NFG) (Close et al. A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms. NeuroImage (2009) vol. 47 (4) pp. 1288-300). This will allow us to thoroughly evaluate the algorithm as well as compare it to various existing connectivity/tractography methods.

[[Image:UNC_GraphbasedConnectivity_Monkeycombined.png|thumb|300px|Average cost map computed on the rhesus monkey DWI atlas with the source located in the internal capsule (left) and the genu of the corpus callosum (right). The grayscale overlays show the FA map, and the 3D visualizations include the streamline tractography results from the same seed regions for comparison purposes.]]

[[Image:UNC_connectivity_nfg.png|thumb|1000px|Application to NFG synthetic data. We start with a complex synthetic dataset, and we compute connectivity maps for each ROI defined by the start/end regions of each fiber bundle. These individual cost maps are compiled into a connectivity matrix that represents the connectivity values between each pair of ROI's. The ROI pairs along the diagonal (marked with a white dot) are the true connections (as given by the ground truth) in the data.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADiffusionGraphBasedConnectivity NA-MIC Publication Database on Graph based connectivity]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Alexis Boucharin, Yundi Shi, Martin Styner

= Links =

[[Category: Diffusion MRI]]

File:UNC connectivity nfg.png

2011-10-17T17:29:55Z

Ipek:

File:UNC GraphbasedConnectivity Monkeycombined.png

2011-04-01T22:15:51Z

Ipek: uploaded a new version of "File:UNC GraphbasedConnectivity Monkeycombined.png"

File:UNC GraphbasedConnectivity Monkeycombined.png

2011-04-01T22:15:37Z

Ipek:

Projects:DiffusionGraphBasedConnectivity

2011-04-01T22:15:05Z

Ipek:

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Muti-directional graph propagation for Diffusion Imaging based Connectivity =

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.

= Description =

[[Image:UNC_GraphbasedConnectivity_Ex1.png|thumb|300px|Slicer visualization of genu connectivity map overlaid on streamline genu tracts]]
Regional connectivity measurements derived from diffusion imaging datasets are of considerable interest in the neuroimaging community for better understanding white matter connectivity. Current connectivity measurements are usually either based on fiber tractography applied in a Monte-Carlo fashion or are variations of the Hamilton-Jacobi approach.

We propose a novel, graph-based algorithm that provides a fully deterministic, efficient and stable connectivity measure. This method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation algorithm applied to sampled orientation distribution functions computed directly from the original diffusion imaging data. A maximum probability and a minimum cost approach are both possible.

So far we have results on synthetic and limited real datasets to illustrate the potential of our method towards subject-specific connectivity measurements performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for studies of neuropathology.

[[Image:UNC_GraphbasedConnectivity_Monkeycombined.png|thumb|300px|Average cost map computed on the rhesus monkey DWI atlas with the source located in the internal capsule (left) and the genu of the corpus callosum (right). The grayscale overlays show the FA map, and the 3D visualizations include the streamline tractography results from the same seed regions for comparison purposes.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADiffusionGraphBasedConnectivity NA-MIC Publication Database on Graph based connectivity]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Alexis Boucharin, Yundi Shi, Martin Styner

= Links =

[[Category: Diffusion MRI]]

Projects:DiffusionGraphBasedConnectivity

2011-04-01T22:11:24Z

Ipek: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Muti-directional graph propagation for Diffusion Imaging based Connectivity =

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.

= Description =

[[Image:UNC_GraphbasedConnectivity_Ex1.png|thumb|300px|Slicer visualization of genu connectivity map overlaid on streamline genu tracts]]
Regional connectivity measurements derived from diffusion imaging datasets are of considerable interest in the neuroimaging community for better understanding white matter connectivity. Current connectivity measurements are usually either based on fiber tractography applied in a Monte-Carlo fashion or are variations of the Hamilton-Jacobi approach.

We propose a novel, graph-based algorithm that provides a fully deterministic, efficient and stable connectivity measure. This method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation algorithm applied to sampled orientation distribution functions computed directly from the original diffusion imaging data. A maximum probability and a minimum cost approach are both possible.

So far we have results on synthetic and limited real datasets to illustrate the potential of our method towards subject-specific connectivity measurements performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for studies of neuropathology.

[[Image:UNC_GraphbasedConnectivity_Monkeycombined.pdf|thumb|300px|Average cost map computed on the rhesus monkey DWI atlas with the source located in the internal capsule (left) and the genu of the corpus callosum (right). The grayscale overlays show the FA map, and the 3D visualizations include the streamline tractography results from the same seed regions for comparison purposes.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADiffusionGraphBasedConnectivity NA-MIC Publication Database on Graph based connectivity]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Alexis Boucharin, Yundi Shi, Martin Styner

= Links =

[[Category: Diffusion MRI]]

Projects:DiffusionGraphBasedConnectivity

2011-04-01T22:09:00Z

Ipek: /* Description */

Back to [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__

= Muti-directional graph propagation for Diffusion Imaging based Connectivity =

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.

= Description =

[[Image:UNC_GraphbasedConnectivity_Ex1.png|thumb|300px|Slicer visualization of genu connectivity map overlaid on streamline genu tracts]]
Regional connectivity measurements derived from diffusion imaging datasets are of considerable interest in the neuroimaging community for better understanding white matter connectivity. Current connectivity measurements are usually either based on fiber tractography applied in a Monte-Carlo fashion or are variations of the Hamilton-Jacobi approach.

We propose a novel, graph-based algorithm that provides a fully deterministic, efficient and stable connectivity measure. This method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation algorithm applied to sampled orientation distribution functions computed directly from the original diffusion imaging data. A maximum probability and a minimum cost approach are both possible.

So far we have results on synthetic and limited real datasets to illustrate the potential of our method towards subject-specific connectivity measurements performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for studies of neuropathology.

[[Image:UNC_GraphbasedConnectivity_Monkeycombined.pdf|thumb|800px|Average cost map computed on the rhesus monkey DWI atlas with the source located in the internal capsule (left) and the genu of the corpus callosum (right). The grayscale overlays show the FA map, and the 3D visualizations include the streamline tractography results from the same seed regions for comparison purposes.]]

= Publications =

* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADiffusionGraphBasedConnectivity NA-MIC Publication Database on Graph based connectivity]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Alexis Boucharin, Yundi Shi, Martin Styner

= Links =

[[Category: Diffusion MRI]]

File:UNC GraphbasedConnectivity Monkeycombined.pdf

2011-04-01T22:07:54Z

Ipek:

Projects:DiffusionGraphBasedConnectivity

2011-04-01T22:03:40Z

Ipek: /* Key Investigators */

Projects:DiffusionGraphBasedConnectivity

2011-04-01T22:03:22Z

Ipek: /* Description */

2010-Slicer-UNCDuke

2010-07-27T20:04:36Z

Ipek: /* Logistics */

== Introduction ==
A one-day Slicer tutorial will be held at UNC and Duke University on August 11, 2010. The primary objective of these 2 Slicer tutorial events is to introduce users at both universities to the Slicer environment, with a particular focus on its use for visualization, fiber tracking, and editing.

== Faculty ==
* [http://www.spl.harvard.edu/~kikinis Ron Kikinis, M.D.]
* Steve Pieper
* Martin Styner
* Ipek Oguz

== Tentative Agenda ==

A 3-3.5 hour tutorial event at each site

* Morning 8:30-12 @ Duke
**NA-MIC kit overview
**Slicer basic visualization
**Slicer interactive editing
**Slicer DTI
* Afternoon 1:30-5pm @ UNC
**NA-MIC kit overview
**Slicer basic visualization
**Slicer interactive editing
**Slicer DTI

== Logistics ==

* To register for the tutorial @ Duke, please email Allan Johnson: gjohnson at duke.edu
* Location @ Duke: TBA
* To register for the tutorial @ UNC, please email Ipek Oguz: ipek at cs.unc.edu
* Location @ UNC: Brooks Building, Rm 141 (Faculty Conference Room)

== Preparations ==

Bring your own laptop with the [http://www.slicer.org/pages/Special:SlicerDownloads Slicer3.6 release version] appropriate to the platform you'll be using.

*Slicer3.6 Software
**Windows: Slicer3-3.6-2010-06-10-win32.exe
**Mac: Slicer3-3.6-2010-06-10-darwin-x86.tar.gz
**Linux 64: Slicer3-3.6-2010-06-10-linux-x86_64.tar.gz
**Linux 32: Slicer3-3.6-2010-06-10-linux-x86.tar.gz

Hardware minimum requirement: 2GB of main memory and graphics hardware acceleration with 256 MB of dedicated graphics memory (nvidia preferred).

*Datasets: We will post the tutorial datasets a few days before the event.

Back to [http://www.na-mic.org/Wiki/index.php/Events NA-MIC Events]

2010-Slicer-UNCDuke

2010-07-27T15:37:10Z

Ipek: /* Logistics */

== Introduction ==
A one-day Slicer tutorial will be held at UNC and Duke University on August 11, 2010. The primary objective of these 2 Slicer tutorial events is to introduce users at both universities to the Slicer environment, with a particular focus on its use for visualization, fiber tracking, and editing.

== Faculty ==
* [http://www.spl.harvard.edu/~kikinis Ron Kikinis, M.D.]
* Steve Pieper
* Martin Styner
* Ipek Oguz

== Tentative Agenda ==

A 3-3.5 hour tutorial event at each site

* Morning 8:30-12 @ Duke
**NA-MIC kit overview
**Slicer basic visualization
**Slicer interactive editing
**Slicer DTI
* Afternoon 1:30-5pm @ UNC
**NA-MIC kit overview
**Slicer basic visualization
**Slicer interactive editing
**Slicer DTI

== Logistics ==

* To register for the tutorial @ UNC, please email ipek at cs.unc.edu
* Location: Brooks Building, Rm 141 (Faculty Conference Room)

== Preparations ==

Bring your own laptop with the [http://www.slicer.org/pages/Special:SlicerDownloads Slicer3.6 release version] appropriate to the platform you'll be using.

*Slicer3.6 Software
**Windows: Slicer3-3.6-2010-06-10-win32.exe
**Mac: Slicer3-3.6-2010-06-10-darwin-x86.tar.gz
**Linux 64: Slicer3-3.6-2010-06-10-linux-x86_64.tar.gz
**Linux 32: Slicer3-3.6-2010-06-10-linux-x86.tar.gz

Hardware minimum requirement: 2GB of main memory and graphics hardware acceleration with 256 MB of dedicated graphics memory (nvidia preferred).

*Datasets: We will post the tutorial datasets a few days before the event.

Back to [http://www.na-mic.org/Wiki/index.php/Events NA-MIC Events]

Projects:CorticalCorrespondenceWithParticleSystem

2009-09-29T16:18:02Z

Ipek: /* Description */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:UNC|UNC Algorithms]], [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Cortical Correspondence with Particle Systems =

[[Image:Sulcaldepth.png|thumb|300px|]]

In this project, we want to compute cortical correspondence on populations,
using various features such as cortical structure, DTI connectivity, vascular
structure. This presents a challenge because of
the highly convoluted surface of the cortex, as well as because of the different
properties of the data features we want to incorporate together.

= Description =

We use a particle-based entropy minimizing system for the correspondence
computation, in a population-based manner. This method best suits
our needs since, parameterization-based methods, such as MDL or SPHARM,
require a spherical parametrization of the surface, which is hard to obtain for
the highly convoluted cortex surface. Another advantage of using the particle-based correspondence technique is that it does not require the surface to be of
spherical topology; this means a lot less pre-processing for our method, since
the brain cortex is not of spherical topology. Another strength of this method is
that it would (eventually) enable correspondence computation on the subcortical structures
and on the cortical surface using the same framework. We also would like to
explore correspondence on the cerebellum, which traditionally is excluded from
such studies (e.g. in FreeSurfer-based work).

The main disadvantage of using the particle-based correspondence technique
on the brain cortex is that it assumes the particles to be existing on local tangent
planes, which presents a challenge for the cortex given the highly convoluted
surface. We propose to overcome this difficulty by first ‘inflating’ the cortex
surface. This way, we obtain a less convoluted, sphere-like surface, where the
particles will be interacting. However, we need a 1-1 correspondence between
the original cortex surface and the inflated surface, since the data to be used
for correspondence, such as the the curvature, and the vascular data, lives on
the original cortex surface. FreeSurfer offers a method that minimizes the distance distortion in the
mapping, while also smoothing the surface. FreeSurfer also preprocesses the
input surface to generate a spherical topology.

[[Image:CaudateProbCorticalConnectivity.jpg|thumb|300px|]]

Once the framework for computing the correspondence given certain data features
is established, the major challenge is to incorporate the various data forms
that we would like to use together. We are using structural data as well as
connectivity (DTI). We have first tested our method using structural metrics, namely, sulcal depth (as computed by FreeSurfer), and have demonstrated improved correspondence quality compared to traditional, location-only correspondence (using the particle-based entropy framework), and we have shown that our results are at least comparable to FreeSurfer. This comes as no surprise as we had already shown in [[Projects:PopulationBasedCorrespondence|our previous studies]] that correspondence can be enhanced by using local curvature in addition of point locations for objects with complex geometry. Next, we have developed a technique for representing the white matter connectivity information on the cortical surface in a manner that can be incorporated into the correspondence framework. We use probabilistic connectivity maps obtained by performing stochastic tractography (which is a [[Projects:DTIStochasticTractography | separate NA-MIC project]]) from various ROI's. In the image to the right, the cortical connectivity for the left and right caudate is visualized in 2 subjects. The top left figure on the bottom image shows this connectivity map overlaid with a coronal slice. The blue outline shows the cortical boundary. Note that the connectivity values on this surface are basically a function of sulcal depth, as illustrated on the far right. We use a cortex deflating scheme to overcome these problems. An example of this deflation process is shown on the bottom row of the figure. The connectivity values on the deflated surface provides a more accurate representation of the DTI data on the surface. We have found that using the connectivity maps in addition to sulcal depth and spatial location further improves correspondence quality.

[[Image:CorticalCorrespondenceIPMI.png|thumb|300px|]]

= Publications =
''In Print''
* [http://www.na-mic.org/publications/pages/display?search=Projects%3ACorticalCorrespondenceWithParticleSystem&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publication Database]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Martin Styner
* Utah Algorithms: Josh Cates, Tom Fletcher, Ross Whitaker

Project Week Results: [[Summer_Project_Week_Slicer3_Cortical_Thickness_Pipeline|June 2009]]

[[Category:fMRI]] [[Category:Shape Analysis]] [[Category: Diffusion MRI]]

File:CorticalCorrespondenceIPMI.png

2009-09-29T16:12:11Z

Ipek:

Projects:CorticalCorrespondenceWithParticleSystem

2009-09-29T16:07:29Z

Ipek: /* Description */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:UNC|UNC Algorithms]], [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Cortical Correspondence with Particle Systems =

[[Image:Sulcaldepth.png|thumb|300px|]]

In this project, we want to compute cortical correspondence on populations,
using various features such as cortical structure, DTI connectivity, vascular
structure. This presents a challenge because of
the highly convoluted surface of the cortex, as well as because of the different
properties of the data features we want to incorporate together.

= Description =

We use a particle-based entropy minimizing system for the correspondence
computation, in a population-based manner. This method best suits
our needs since, parameterization-based methods, such as MDL or SPHARM,
require a spherical parametrization of the surface, which is hard to obtain for
the highly convoluted cortex surface. Another advantage of using the particle-based correspondence technique is that it does not require the surface to be of
spherical topology; this means a lot less pre-processing for our method, since
the brain cortex is not of spherical topology. Another strength of this method is
that it would (eventually) enable correspondence computation on the subcortical structures
and on the cortical surface using the same framework. We also would like to
explore correspondence on the cerebellum, which traditionally is excluded from
such studies (e.g. in FreeSurfer-based work).

The main disadvantage of using the particle-based correspondence technique
on the brain cortex is that it assumes the particles to be existing on local tangent
planes, which presents a challenge for the cortex given the highly convoluted
surface. We propose to overcome this difficulty by first ‘inflating’ the cortex
surface. This way, we obtain a less convoluted, sphere-like surface, where the
particles will be interacting. However, we need a 1-1 correspondence between
the original cortex surface and the inflated surface, since the data to be used
for correspondence, such as the the curvature, and the vascular data, lives on
the original cortex surface. FreeSurfer offers a method that minimizes the distance distortion in the
mapping, while also smoothing the surface. FreeSurfer also preprocesses the
input surface to generate a spherical topology.

[[Image:CaudateProbCorticalConnectivity.jpg|thumb|300px|]]

Once the framework for computing the correspondence given certain data features
is established, the major challenge is to incorporate the various data forms
that we would like to use together. We are using structural data as well as
connectivity (DTI). We have first tested our method using structural metrics, namely, sulcal depth (as computed by FreeSurfer), and have demonstrated improved correspondence quality compared to traditional, location-only correspondence (using the particle-based entropy framework), and we have shown that our results are at least comparable to FreeSurfer. This comes as no surprise as we had already shown in [[Projects:PopulationBasedCorrespondence|our previous studies]] that correspondence can be enhanced by using local curvature in addition of point locations for objects with complex geometry. Next, we have developed a technique for representing the white matter connectivity information on the cortical surface in a manner that can be incorporated into the correspondence framework. We use probabilistic connectivity maps obtained by performing stochastic tractography (which is a [[Projects:DTIStochasticTractography | separate NA-MIC project]]) from various ROI's. In the image to the right, the cortical connectivity for the left and right caudate is visualized in 2 subjects.We use a cortex deflating scheme to overcome problems related to noisy DTI data near the cortical boundary. We have found that using the connectivity maps in addition to sulcal depth and spatial location further improves correspondence quality.

= Publications =
''In Print''
* [http://www.na-mic.org/publications/pages/display?search=Projects%3ACorticalCorrespondenceWithParticleSystem&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publication Database]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Martin Styner
* Utah Algorithms: Josh Cates, Tom Fletcher, Ross Whitaker

Project Week Results: [[Summer_Project_Week_Slicer3_Cortical_Thickness_Pipeline|June 2009]]

[[Category:fMRI]] [[Category:Shape Analysis]] [[Category: Diffusion MRI]]

NA-MIC Internal Collaborations:StructuralImageAnalysis

2009-05-07T20:53:36Z

Ipek: /* Cortical Correspondence using Particle System */

Back to [[NA-MIC_Internal_Collaborations|NA-MIC Internal Collaborations]]
__NOTOC__
= Structural Image Analysis =

=== Image Segmentation ===

{| cellpadding="10"
| style="width:15%" | [[Image:ProstateDiagram.png|200px]]
| style="width:85%" |

== [[ProjectWeek200706:BrachytherapyNeedlePositioningRobotIntegration|Brachytherapy Needle Positioning Robot Integration]] ==

The Queen’s/Hopkins team is developing novel devices and procedures for cancer interventions, including biopsy and therapies. Our goal for the programming week is to design and start implementing software for the new MRI Brachytherapy needle positioning robot. [[ProjectWeek200706:BrachytherapyNeedlePositioningRobotIntegration|More...]]

<font color="red">'''New: '''</font> Meeting at JHU on July 17-19, 2007.

|-

| | [[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...]]

<font color="red">'''New: '''</font> J. Melonakos, Y. Gao, and A. Tannenbaum. Tissue Tracking: Applications for Brain MRI Classification. SPIE Medical Imaging, 2007.

|-

| | [[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...]]

|-

| | [[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...]]

<font color="red">'''New: '''</font> Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007.

|-

| | [[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...]]

<font color="red">'''New: '''</font>Al-Hakim R, Nain D, Melonakos J, Tannenbaum A, Fallon J. [http://www.na-mic.org/pages/Special:PubDB_View?dspaceid=320 A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter.] Proc SPIE Medical Imaging, 2006.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
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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...]]

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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...]]

<font color="red">'''New: '''</font> Delphine Nain won the best student paper at [[MICCAI_2006|MICCAI 2006]] in the category "Segmentation and Registration" for her paper entitled "Shape-driven surface segmentation using spherical wavelets" by D. Nain, S. Haker, A. Bobick, A. Tannenbaum.

|-

| | [[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...]]

<font color="red">'''New: '''</font> Currently under investigation.

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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:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

T Tasdizen, S Awate, R Whitaker, A nonparametric, entropy-minimizing MRI tissue classification algorithm implementation using ITK, MICCAI 2005 Open-Source Workshop.

|-

| | [[Image:histo_matching.jpg|200px]]
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== [[Projects:AutomaticFullBrainSegmentation|Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms]] ==

Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Projects:AutomaticFullBrainSegmentation|More...]]

<font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

|}

=== Image Registration ===

{| cellpadding="10"
| style="width:15%" | [[Image:Results brain sag.JPG|200px]]
| style="width:85%" |

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

The goal of this project is to implement a computationaly 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> Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. SPIE Conference on Computational Imaging V, Jan 2007.

|-

| | [[Image:DTIregistration200.png|200px]]
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== [[Projects:DTIProcessingTools|Diffusion Tensor Image Processing Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

<font color="red">'''New: '''</font> We have recently developed software for eddy current correction.

|-

| | [[Image:EPIT1Registration.png|height="200px"]]
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== [[Projects:NonRigidEPIRegistration|Non-Rigid EPI Registration]] ==

Our Objective is to identify optimal ITK method and parameter settings for non-rigid intrasubject registration of T2 EPI, the raw building block images of DTI, to T1 conventional images. Provide software devliverable. [[Projects:NonRigidEPIRegistration|More...]]

<font color="red">'''New: '''</font> Project Week Results: [[Engineering:Project:Non-rigid_EPI_registration|Jan 2006]]

|-

| | [[Image:Sulcaldepth.png|200px]]
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== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

<font color="red">'''New: '''</font>

* IPMI 2009 paper
* Incorporating DTI data in cortical correspondence completed.

|-

| | [[Image:Cbg-dtiatlas-tracts.png|200px]]
| |

== [[Projects:DTIPopulationAnalysis|Population Analysis from Deformable Registration]] ==

Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics. This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. [[Projects:DTIPopulationAnalysis|More...]]

<font color="red">'''New: '''</font> Command line DTI tools available as part of UNC [http://www.ia.unc.edu/dev NeuroLib]

|-

| | [[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. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:JointRegSeg.png|200px]]
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== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. In Proceedings of MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, 683-691, 2007. '''MICCAI Young Scientist Award.'''

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| | [[Image:ICluster_templates.gif|200px]]
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== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

<font color="red">'''New: '''</font> M.R. Sabuncu, M.E. Shenton, P. Golland. Joint Registration and Clustering of Images. In Proceedings of MICCAI 2007 Statistical Registration Workshop: Pair-wise and Group-wise Alignment and Atlas Formation, 47-54, 2007.

|-

| | [[Image:GroupwiseSummary.PNG|200px]]
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== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.
[[Projects:GroupwiseRegistration|More...]]

<font color="red">'''New:'''</font> S.K. Balci, P. Golland, M.E. Shenton, W.M. Wells III. Free-Form B-spline Deformation Model for Groupwise Registration. In Proceedings of MICCAI 2007 Statistical Registration Workshop: Pair-wise and Group-wise Alignment and Atlas Formation, 23-30, 2007.

|}

=== Morphometric Measures and Shape Analysis ===

{| cellpadding="10"
| style="width:15%" | [[Image:Basis membership.png|200px]]
| style="width:85%" |

== [[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...]]

<font color="red">'''New: '''</font> D. Nain, M. Styner, M. Niethammer, J. J. Levitt, M E Shenton, G Gerig, A. Bobick, A. Tannenbaum. Statistical Shape Analysis of Brain Structures using Spherical Wavelets. Accepted in The Fourth IEEE International Symposium on Biomedical Imaging (ISBI ’07) that will be held April 12-15, 2007 in Metro Washington DC, USA.

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| | [[Image:HippocampalShapeDifferences.gif|200px]]
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== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

<font color="red">'''New:'''</font> Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.

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| | [[Image:FoldingSpeedDetection.png|200px]]
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== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. Accepted to the IEEE Transactions on Image Processing.

P. Yu, B.T.T. Yeo, P.E. Grant, B. Fischl, P. Golland. Cortical Folding Development Study based on Over-Complete Spherical Wavelets. In Proceedings of MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2007.

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== [[Projects:ShapeBasedLevelSetSegmentation|Shape Based Level Segmentation]] ==

This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. [[Projects:ShapeBasedLevelSetSegmentation|More...]]

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| | [[Image:Progress_Registration_Segmentation_Shape.jpg|200px]]
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== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

<font color="red">'''New: '''</font> K.M. Pohl, J. Fisher, S. Bouix, M. Shenton, R. W. McCarley, W.E.L. Grimson, R. Kikinis, and W.M. Wells. Using the Logarithm of Odds to Define a Vector Space on Probabilistic Atlases. Medical Image Analysis,11(6), pp. 465-477, 2007. <b>Best Paper Award MICCAI 2006 </b>

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| | [[Image:Meanviews.png|200px]]
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== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

<font color="red">'''New: '''</font> J Cates, PT Fletcher, M Styner, M Shenton, R Whitaker, Shape modeling and analysis with entropy-based particle systems, IPMI 2007, pp. 333-345.

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| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
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== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

<font color="red">'''New: '''</font>

* First version of Shape Analysis Toolset available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]) , this is to be added to the NAMIC toolkit.
* Slicer 3 module for whole shape analysis pipeline in progress (based on BatchMake and distributed computing using Condor)

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| | [[Image:ShapeAnalysisOverviewStatsHippo05.jpg|200px]]
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== [[Projects:ShapeAnalysisOfHippocampus|Shape Analysis of the Hippocampus]] ==

Our objective is to examine hippocampal shape in patients with schizophrenia and healthy controls. [[Projects:ShapeAnalysisOfHippocampus|More...]]

<font color="red">'''New: '''</font> Styner M, Lieberman JA, McClure RK, Weinberger DR, Jones DW, Gerig G.: Morphometric analysis of lateral ventricles in schizophrenia and healthy controls regarding genetic and disease-specific factors, Proc Natl Acad Sci USA. 2005 Mar 29;102(13):4872-7. Epub 2005 Mar 16.

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| | [[Image:UNCShape_ShapeCorrespondence.png|200px]]
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== [[Projects:PopulationBasedCorrespondence|Population Based Correspondence]] ==

We are developing methodology to automatically find dense point correspondences between a collection of polygonal genus 0 meshes. The advantage of this method is independence from indivisual templates, as well as enhanced modeling properties. The method is based on minimizing a cost function that describes the goodness of correspondence. Apart from a cost function derived from the description length of the model, we also employ a cost function working with arbitrary local features. We extended the original methods to use surface curvature measurements, which are independent to differences of object aligment. [[Projects:PopulationBasedCorrespondence|More...]]

<font color="red">'''New: '''</font>

* Software available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev website])
* Evaluation on lateral ventricles, hippocampi, caudates, striatum, femural bone. Outperforms standard MDL on complex structures.

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| | [[Image:overcomplete_vs_biorthogonal_wavelets.jpg|200px]]
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== [[Projects:CorticalSurfaceShapeAnalysisUsingSphericalWavelets|Spherical Wavelets]] ==
Cortical Surface Shape Analysis Based on Spherical Wavelets. We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been proved to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant to rotation of the underlying surface parameterization. In this paper, we demonstrate the theoretical advantage of over-complete wavelets over bi-orthogonal wavelets and illustrate their utility on both synthetic and real data. In particular, we show that the over-complete spherical wavelet transform enjoys significant advantages for the analysis of cortical folding development in a newborn dataset. [[Projects:CorticalSurfaceShapeAnalysisUsingSphericalWavelets|More...]]

<font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

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| | [[Image:separating_loops.jpg|200px]]
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== [[Projects:TopologyCorrectionNonSeparatingLoops|Topology Correction]] ==

Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically,we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator. [[Projects:TopologyCorrectionNonSeparatingLoops|More...]]

<font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

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| | [[Image:UNCShape_CaudatePval_MICCAI06.png|200px]]
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== [[Projects:LocalStatisticalAnalysisViaPermutationTests|Local Statistical Analysis via Permutation Tests]] ==

We have further developed a set of statistical testing methods that allow the analysis of local shape differences using the Hotelling T 2 two sample metric. Permutatioin tests are employed for the computation of statistical p-values, both raw and corrected for multiple comparisons. Resulting significance maps are easily visualized. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. Ongoing research focuses on incorporating covariates such as clinical scores into the testing scheme. [[Projects:LocalStatisticalAnalysisViaPermutationTests|More...]]

<font color="red">'''New: '''</font>

* Available as part of Shape Analysis Toolset in UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]).

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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:qdec.jpg|200px]]
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== [[Projects:QDEC|QDEC: An easy to use GUI for group morphometry studies]] ==

Qdec is a application included in the Freesurfer software package intended to aid researchers in performing inter-subject / group averaging and inference on the morphometry data (cortical surface and volume) produced by the Freesurfer processing stream. The functionality in Qdec is also available as a processing module within Slicer3, and XNAT. [[Projects:QDEC|More...]]

See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]

|}

Projects:CorticalCorrespondenceWithParticleSystem

2009-04-16T22:15:46Z

Ipek: /* Description */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:UNC|UNC Algorithms]], [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Cortical Correspondence with Particle Systems =

[[Image:Sulcaldepth.png|thumb|300px|]]

In this project, we want to compute cortical correspondence on populations,
using various features such as cortical structure, DTI connectivity, vascular
structure. This presents a challenge because of
the highly convoluted surface of the cortex, as well as because of the different
properties of the data features we want to incorporate together.

= Description =

We would like to use a particle-based entropy minimizing system for the correspondence
computation, in a population-based manner. This method best suits
our needs since, parameterization-based methods, such as MDL or SPHARM,
require a spherical parametrization of the surface, which is hard to obtain for
the highly convoluted cortex surface. Another advantage of using the particle-based correspondence technique is that it does not require the surface to be of
spherical topology; this means a lot less pre-processing for our method, since
the brain cortex is not of spherical topology. Another strength of this method is
that it would (eventually) enable correspondence computation on the subcortical structures
and on the cortical surface using the same framework. We also would like to
explore correspondence on the cerebellum, which traditionally is excluded from
such studies (e.g. in FreeSurfer-based work).

The main disadvantage of using the particle-based correspondence technique
on the brain cortex is that it assumes the particles to be existing on local tangent
planes, which presents a challenge for the cortex given the highly convoluted
surface. We propose to overcome this difficulty by first ‘inflating’ the cortex
surface. This way, we obtain a less convoluted, sphere-like surface, where the
particles will be interacting. However, we need a 1-1 correspondence between
the original cortex surface and the inflated surface, since the data to be used
for correspondence, such as the the curvature, and the vascular data, lives on
the original cortex surface. FreeSurfer offers a method that minimizes the distance distortion in the
mapping, while also smoothing the surface. FreeSurfer also preprocesses the
input surface to generate a spherical topology.

[[Image:CaudateProbCorticalConnectivity.jpg|thumb|300px|]]

Once the framework for computing the correspondence given certain data features
is established, the major challenge is to incorporate the various data forms
that we would like to use together. We are using structural data as well as
connectivity (DTI). We have first tested our method using structural metrics, namely, sulcal depth (as computed by FreeSurfer), and have demonstrated improved correspondence quality compared to traditional, location-only correspondence (using the particle-based entropy framework), and we have shown that our results are at least comparable to FreeSurfer. This comes as no surprise as we had already shown in [[Projects:PopulationBasedCorrespondence|our previous studies]] that correspondence can be enhanced by using local curvature in addition of point locations for objects with complex geometry. Next, we have developed a technique for representing the white matter connectivity information on the cortical surface in a manner that can be incorporated into the correspondence framework. We use probabilistic connectivity maps obtained by performing stochastic tractography (which is a [[Projects:DTIStochasticTractography | separate NA-MIC project]]) from various ROI's. In the image to the right, the cortical connectivity for the left and right caudate is visualized in 2 subjects.We use a cortex deflating scheme to overcome problems related to noisy DTI data near the cortical boundary. We have found that using the connectivity maps in addition to sulcal depth and spatial location further improves correspondence quality.

= Publications =
* Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009, in print.
*[http://www.na-mic.org/pages/Special:PubDB_View?dspaceid=1444 Oguz I, Cates J, Fletcher T, Whitaker R, Cool D, Aylward S, Styner M. Cortical correspondence using entropy-based particle systems and local features. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1637-1640.]
* [http://www.na-mic.org/pages/Special:Publications?text=Projects%3ACorticalCorrespondenceWithParticleSystem&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| Cates J, Fletcher P, Styner M, Shenton M, Whitaker R. Shape modeling and analysis with entropy-based particle systems. Inf Process Med Imaging. 2007;20:333-45.]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Martin Styner
* Utah Algorithms: Josh Cates, Tom Fletcher, Ross Whitaker

[[Category:fMRI]] [[Category:Shape Analysis]] [[Category: Diffusion MRI]]

Projects:CorticalCorrespondenceWithParticleSystem

2009-04-16T18:58:55Z

Ipek: /* Publications */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:UNC|UNC Algorithms]], [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Cortical Correspondence with Particle Systems =

[[Image:Sulcaldepth.png|thumb|300px|]]

In this project, we want to compute cortical correspondence on populations,
using various features such as cortical structure, DTI connectivity, vascular
structure. This presents a challenge because of
the highly convoluted surface of the cortex, as well as because of the different
properties of the data features we want to incorporate together.

= Description =

We would like to use a particle based entropy minimizing system for the correspondence
computation, in a population-based manner. This method best suits
our needs since, parametrization based methods, such as MDL or SPHARM,
require a spherical parametrization of the surface, which is hard to obtain for
the highly convoluted cortex surface. Another advantage of using the particle
based correspondence technique is that it does not require the surface to be of
spherical topology; this means a lot less pre-processing for our method, since
the brain cortex is not of spherical topology. Another strength of this method is
that it would (eventually) enable correspondence computation on the subcortical structures
and on the cortical surface using the same framework. We also would like to
explore correspondence on the cerebellum, which traditionally is excluded from
such studies (e.g. in FreeSurfer-based work).

The main disadvantage of using the particle based correspondence technique
on the brain cortex is that it assumes the particles to be existing on local tangent
planes, which presents a challenge for the cortex given the highly convoluted
surface. We are hoping to overcome this difficulty by first ‘inflating’ the cortex
surface. This way, we obtain a less convoluted, sphere-like surface, where the
particles will be interacting. However, we need a 1-1 correspondence between
the original cortex surface and the inflated surface, since the data to be used
for correspondence, such as the the curvature, and the vascular data, lives on
the original cortex surface. FreeSurfer offers a method that minimizes the distance distortion in the
mapping, while also smoothing the surface. FreeSurfer also preprocesses the
input surface to generate a spherical topology.

[[Image:CaudateProbCorticalConnectivity.jpg|thumb|300px|]]

Once the framework for computing the correspondence given certain data features
is established, the major challenge is to incorporate the various data forms
that we would like to use together. We are planning on using structural data as well as
connectivity (DTI). So far, we have tested our method using structural metrics, namely, sulcal depth (as computed by FreeSurfer), and have demonstrated improved correspondence quality compared to traditional, location-only correspondence (using the particle-based entropy framework), and we have shown that our results are at least comparable to FreeSurfer. This comes as no surprise as we had already shown in [[Projects:PopulationBasedCorrespondence|our previous studies]] that correspondence can be enhanced by using local curvature in addition of point locations for objects with complex geometry. Currently, we are at finalizing the use of connectivity information obtained with stochastic tractography methods developed as part of a [[Projects:DTIStochasticTractography | separate NA-MIC project]]. In the image to the right, the cortical connectivity for the left and right caudate is visualized in 2 subjects.

= Publications =
* Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009, in print.
*[http://www.na-mic.org/pages/Special:PubDB_View?dspaceid=1444 Oguz I, Cates J, Fletcher T, Whitaker R, Cool D, Aylward S, Styner M. Cortical correspondence using entropy-based particle systems and local features. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1637-1640.]
* [http://www.na-mic.org/pages/Special:Publications?text=Projects%3ACorticalCorrespondenceWithParticleSystem&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| Cates J, Fletcher P, Styner M, Shenton M, Whitaker R. Shape modeling and analysis with entropy-based particle systems. Inf Process Med Imaging. 2007;20:333-45.]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Martin Styner
* Utah Algorithms: Josh Cates, Tom Fletcher, Ross Whitaker

[[Category:fMRI]] [[Category:Shape Analysis]] [[Category: Diffusion MRI]]

Projects:CorticalCorrespondenceWithParticleSystem

2009-04-16T18:55:55Z

Ipek: /* Cortical Correspondence with Particle Systems */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:UNC|UNC Algorithms]], [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Cortical Correspondence with Particle Systems =

[[Image:Sulcaldepth.png|thumb|300px|]]

In this project, we want to compute cortical correspondence on populations,
using various features such as cortical structure, DTI connectivity, vascular
structure. This presents a challenge because of
the highly convoluted surface of the cortex, as well as because of the different
properties of the data features we want to incorporate together.

= Description =

We would like to use a particle based entropy minimizing system for the correspondence
computation, in a population-based manner. This method best suits
our needs since, parametrization based methods, such as MDL or SPHARM,
require a spherical parametrization of the surface, which is hard to obtain for
the highly convoluted cortex surface. Another advantage of using the particle
based correspondence technique is that it does not require the surface to be of
spherical topology; this means a lot less pre-processing for our method, since
the brain cortex is not of spherical topology. Another strength of this method is
that it would (eventually) enable correspondence computation on the subcortical structures
and on the cortical surface using the same framework. We also would like to
explore correspondence on the cerebellum, which traditionally is excluded from
such studies (e.g. in FreeSurfer-based work).

The main disadvantage of using the particle based correspondence technique
on the brain cortex is that it assumes the particles to be existing on local tangent
planes, which presents a challenge for the cortex given the highly convoluted
surface. We are hoping to overcome this difficulty by first ‘inflating’ the cortex
surface. This way, we obtain a less convoluted, sphere-like surface, where the
particles will be interacting. However, we need a 1-1 correspondence between
the original cortex surface and the inflated surface, since the data to be used
for correspondence, such as the the curvature, and the vascular data, lives on
the original cortex surface. FreeSurfer offers a method that minimizes the distance distortion in the
mapping, while also smoothing the surface. FreeSurfer also preprocesses the
input surface to generate a spherical topology.

[[Image:CaudateProbCorticalConnectivity.jpg|thumb|300px|]]

Once the framework for computing the correspondence given certain data features
is established, the major challenge is to incorporate the various data forms
that we would like to use together. We are planning on using structural data as well as
connectivity (DTI). So far, we have tested our method using structural metrics, namely, sulcal depth (as computed by FreeSurfer), and have demonstrated improved correspondence quality compared to traditional, location-only correspondence (using the particle-based entropy framework), and we have shown that our results are at least comparable to FreeSurfer. This comes as no surprise as we had already shown in [[Projects:PopulationBasedCorrespondence|our previous studies]] that correspondence can be enhanced by using local curvature in addition of point locations for objects with complex geometry. Currently, we are at finalizing the use of connectivity information obtained with stochastic tractography methods developed as part of a [[Projects:DTIStochasticTractography | separate NA-MIC project]]. In the image to the right, the cortical connectivity for the left and right caudate is visualized in 2 subjects.

= Publications =
*[http://www.na-mic.org/pages/Special:PubDB_View?dspaceid=1444 Oguz I, Cates J, Fletcher T, Whitaker R, Cool D, Aylward S, Styner M. Cortical correspondence using entropy-based particle systems and local features. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1637-1640.]
* [http://www.na-mic.org/pages/Special:Publications?text=Projects%3ACorticalCorrespondenceWithParticleSystem&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| Cates J, Fletcher P, Styner M, Shenton M, Whitaker R. Shape modeling and analysis with entropy-based particle systems. Inf Process Med Imaging. 2007;20:333-45.]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Martin Styner
* Utah Algorithms: Josh Cates, Tom Fletcher, Ross Whitaker

[[Category:fMRI]] [[Category:Shape Analysis]] [[Category: Diffusion MRI]]

Algorithm:UNC

2009-04-16T18:55:13Z

Ipek: /* Cortical Correspondence using Particle System */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of UNC Algorithms (PI: Martin Styner) =

At UNC, we are interested in a range of algorithms and solutions for the surface based analysis of brain structures and the cortex. We pioneered the use of spherical harmonics based shape analysis for comparing brain structures across objects. We are now working on incorporating various data sources on the entire cortical surface for improving the correspondence computation. Furthermore, validation and evaluation of methods is highly relevant within our core.

= UNC Projects =

{| cellpadding="10"

| style="width:15%" |[[Image:Cause07Competition.gif|200px]]
| style="width:85%" |

== [[Projects:MethodEvaluationValidation|Evaluation and Comparison of Medical Image Analysis Methods]] ==

In this project, we want to focus on the evaluation of medical image analysis methods for specific clinical applications in respect to development of evaluation methodology and the organization of venues promoting such comparison and validation studies.

<font color="red">'''New: '''</font>

* Co-Organization of MICCAI 2008 workshop on [http://grand-challenge2008.bigr.nl "3D Segmentation in the Clinic II- A Grand Challenge"] with a competition on Multiple Sclerosis lesion segmentation
* Continuation of workshop competition on the [http://www.ia.unc.edu/MSseg/ MS segmentation online comparison]

|-

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

== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

<font color="red">'''New: '''</font>

* IPMI 2009 paper
* Incorporating DTI data in cortical correspondence completed.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

<font color="red">'''New: '''</font>
* new PLoS One paper
* First version of Shape Analysis Toolset available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]) , this is to be added to the NAMIC toolkit.
* Slicer 3 module for whole shape analysis pipeline in progress (based on BatchMake and distributed computing using Condor)

|-

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

== [[Projects:PopulationBasedCorrespondence|Population Based Correspondence]] ==

We are developing methodology to automatically find dense point correspondences between a collection of polygonal genus 0 meshes. The advantage of this method is independence from indivisual templates, as well as enhanced modeling properties. The method is based on minimizing a cost function that describes the goodness of correspondence. Apart from a cost function derived from the description length of the model, we also employ a cost function working with arbitrary local features. We extended the original methods to use surface curvature measurements, which are independent to differences of object aligment. [[Projects:PopulationBasedCorrespondence|More...]]

<font color="red">'''New: '''</font>
* ISBI paper and podium presentation
* Software available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev website])
* Evaluation on lateral ventricles, hippocampi, caudates, striatum, femural bone. Outperforms standard MDL on complex structures.

|-

| | [[Image:UNCShape_CaudatePval_MICCAI06.png|200px]]
| |

== [[Projects:LocalStatisticalAnalysisViaPermutationTests|Local Statistical Analysis via Permutation Tests]] ==

We have further developed a set of statistical testing methods that allow the analysis of local shape differences using the Hotelling T 2 two sample metric. Permutatioin tests are employed for the computation of statistical p-values, both raw and corrected for multiple comparisons. Resulting significance maps are easily visualized. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. Ongoing research focuses on incorporating covariates such as clinical scores into the testing scheme. [[Projects:LocalStatisticalAnalysisViaPermutationTests|More...]]

<font color="red">'''New: '''</font>

* Available as part of Shape Analysis Toolset in UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]).

|}

2009 Winter Project Week Cortical Correspondence

2009-01-09T15:56:35Z

Ipek: /* Key Investigators */

{|
|[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2009_Winter_Project_Week|Project Week Main Page]] ]]
|[[Image:Sulcaldepth.png |thumb|320px|Particles on an inflated cortex - the color map shows the local sulcal depth measurements]]
|}

__NOTOC__

===Key Investigators===
* UNC: Ipek Oguz, Martin Styner
* Kitware: Will Schroeder
* GE: Xiaodong Tao

<div style="margin: 20px;">

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

<h1>Objective</h1>

We are developing a cortical correspondence framework that incorporates data from structural images as well as DTI connectivity. The goal at this project week is to create Slicer modules for the entire correspondence pipeline, where the input is surface meshes and local attributes at vertices, and the output is meshes with optimized vertex correspondence.

</div>

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

<h1>Approach, Plan</h1>

The correspondence pipeline consists of three stages. The first stage scan converts each input surface into a volume, and creates a signed distance map (the surface is the zero-level set of the distance map). It also interpolates the local attributes defined on the mesh vertices to obtain attribute volumes that match the distance volumes. The plan for the project week is to use the VTK point data reader/writer for the attributes.

The particle correspondence framework (the second stage) works on volumes containing distance maps to the surface, as well as attribute volumes. The output of the correspondence tool is a collection of particle locations defined on each surface, such that same index particles correspond across the population. The plan for the project week here is to figure out the best file format for storing this data, and adapting the modules accordingly.

The last stage of the pipeline takes the original meshes and the corresponding particle locations to create new meshes with corresponding vertex locations. The plan for the project week is to create a VTK filter for this task.

</div>

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

<h1>Progress</h1>

Slicer modules for each of the scan conversion, particle correspondence, and re-meshing stages are already created.

We tested the new python implementation of the stochastic tractography algorithm on our data successfully.

We discussed with the Utah group how to divide the different parts of the pipeline to be as modular as possible, and what the input/output of each module should be.

</div>

<br style="clear: both;" />

</div>

===References===
*Cortical Correspondence Using Entropy-Based Particle Systems and Local Features, Oguz I, Cates J, Fletcher T, Whitaker R, Cool D, Aylward S, Styner M, IEEE Symposium on Biomedical Imaging ISBI 2008, pp. 1637- 1640.
*J. Cates, T. Fletcher, and R. Whitaker, "Entropy-based particle systems for shape correspondence," Mathematical Foundations of Computational Anatomy Workshop, MICCAI 2006, pp. 90–99, Oct. 2006.
*J. Cates, T. Fletcher, M. Styner, M. Shenton, and R. Whitaker, “Shape modeling and analysis with entropy-based particle systems,” in IPMI, 2007, pp. 333–345.

2009 Winter Project Week Cortical Correspondence

2009-01-09T15:56:04Z

Ipek: /* Key Investigators */

{|
|[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2009_Winter_Project_Week|Project Week Main Page]] ]]
|[[Image:Sulcaldepth.png |thumb|320px|Particles on an inflated cortex - the color map shows the local sulcal depth measurements]]
|}

__NOTOC__

===Key Investigators===
* UNC: Ipek Oguz, Martin Styner
* Kitware: Will Schroeder
* GE: Xiaodong Tao

<div style="margin: 20px;">

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

<h1>Objective</h1>

We are developing a cortical correspondence framework that incorporates data from structural images as well as DTI connectivity. The goal at this project week is to create Slicer modules for the entire correspondence pipeline, where the input is surface meshes and local attributes at vertices, and the output is meshes with optimized vertex correspondence.

</div>

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

<h1>Approach, Plan</h1>

The correspondence pipeline consists of three stages. The first stage scan converts each input surface into a volume, and creates a signed distance map (the surface is the zero-level set of the distance map). It also interpolates the local attributes defined on the mesh vertices to obtain attribute volumes that match the distance volumes. The plan for the project week is to use the VTK point data reader/writer for the attributes.

The particle correspondence framework (the second stage) works on volumes containing distance maps to the surface, as well as attribute volumes. The output of the correspondence tool is a collection of particle locations defined on each surface, such that same index particles correspond across the population. The plan for the project week here is to figure out the best file format for storing this data, and adapting the modules accordingly.

The last stage of the pipeline takes the original meshes and the corresponding particle locations to create new meshes with corresponding vertex locations. The plan for the project week is to create a VTK filter for this task.

</div>

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

<h1>Progress</h1>

Slicer modules for each of the scan conversion, particle correspondence, and re-meshing stages are already created.

We tested the new python implementation of the stochastic tractography algorithm on our data successfully.

We also discussed, with the Utah group, how to divide the different parts of the pipeline to be as modular as possible, and what the input/output of each module should be.

</div>

<br style="clear: both;" />

</div>

===References===
*Cortical Correspondence Using Entropy-Based Particle Systems and Local Features, Oguz I, Cates J, Fletcher T, Whitaker R, Cool D, Aylward S, Styner M, IEEE Symposium on Biomedical Imaging ISBI 2008, pp. 1637- 1640.
*J. Cates, T. Fletcher, and R. Whitaker, "Entropy-based particle systems for shape correspondence," Mathematical Foundations of Computational Anatomy Workshop, MICCAI 2006, pp. 90–99, Oct. 2006.
*J. Cates, T. Fletcher, M. Styner, M. Shenton, and R. Whitaker, “Shape modeling and analysis with entropy-based particle systems,” in IPMI, 2007, pp. 333–345.

2009 Winter Project Week Cortical Correspondence

2008-12-18T02:05:47Z

Ipek:

{|
|[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2009_Winter_Project_Week|Project Week Main Page]] ]]
|[[Image:Sulcaldepth.png |thumb|320px|Particles on an inflated cortex - the color map shows the local sulcal depth measurements]]
|}

__NOTOC__

===Key Investigators===
* UNC: Ipek Oguz, Martin Styner
* Kitware: Will Schroeder
* GE: Xiaodong Tao

<div style="margin: 20px;">

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

<h1>Objective</h1>

We are developing a cortical correspondence framework that incorporates data from structural images as well as DTI connectivity. The goal at this project week is to create Slicer modules for the entire correspondence pipeline, where the input is surface meshes and local attributes at vertices, and the output is meshes with optimized vertex correspondence.

</div>

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

<h1>Approach, Plan</h1>

The correspondence pipeline consists of three stages. The first stage scan converts each input surface into a volume, and creates a signed distance map (the surface is the zero-level set of the distance map). It also interpolates the local attributes defined on the mesh vertices to obtain attribute volumes that match the distance volumes. The plan for the project week is to use the VTK point data reader/writer for the attributes.

The particle correspondence framework (the second stage) works on volumes containing distance maps to the surface, as well as attribute volumes. The output of the correspondence tool is a collection of particle locations defined on each surface, such that same index particles correspond across the population. The plan for the project week here is to figure out the best file format for storing this data, and adapting the modules accordingly.

The last stage of the pipeline takes the original meshes and the corresponding particle locations to create new meshes with corresponding vertex locations. The plan for the project week is to create a VTK filter for this task.

</div>

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

<h1>Progress</h1>

Slicer modules for each of the scan conversion, particle correspondence, and re-meshing stages are already created.

</div>

<br style="clear: both;" />

</div>

===References===
*Cortical Correspondence Using Entropy-Based Particle Systems and Local Features, Oguz I, Cates J, Fletcher T, Whitaker R, Cool D, Aylward S, Styner M, IEEE Symposium on Biomedical Imaging ISBI 2008, pp. 1637- 1640.
*J. Cates, T. Fletcher, and R. Whitaker, "Entropy-based particle systems for shape correspondence," Mathematical Foundations of Computational Anatomy Workshop, MICCAI 2006, pp. 90–99, Oct. 2006.
*J. Cates, T. Fletcher, M. Styner, M. Shenton, and R. Whitaker, “Shape modeling and analysis with entropy-based particle systems,” in IPMI, 2007, pp. 333–345.

2009 Winter Project Week Cortical Correspondence

2008-12-18T02:05:14Z

Ipek:

{|
|[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2009_Winter_Project_Week|Project Week Main Page]] ]]
|[[Image:Sulcaldepth.png |thumb|320px|Particles on an inflated cortex - the color map shows the local sulcal depth measurements]]|}

__NOTOC__

===Key Investigators===
* UNC: Ipek Oguz, Martin Styner
* Kitware: Will Schroeder
* GE: Xiaodong Tao

<div style="margin: 20px;">

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

<h1>Objective</h1>

We are developing a cortical correspondence framework that incorporates data from structural images as well as DTI connectivity. The goal at this project week is to create Slicer modules for the entire correspondence pipeline, where the input is surface meshes and local attributes at vertices, and the output is meshes with optimized vertex correspondence.

</div>

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

<h1>Approach, Plan</h1>

The correspondence pipeline consists of three stages. The first stage scan converts each input surface into a volume, and creates a signed distance map (the surface is the zero-level set of the distance map). It also interpolates the local attributes defined on the mesh vertices to obtain attribute volumes that match the distance volumes. The plan for the project week is to use the VTK point data reader/writer for the attributes.

The particle correspondence framework (the second stage) works on volumes containing distance maps to the surface, as well as attribute volumes. The output of the correspondence tool is a collection of particle locations defined on each surface, such that same index particles correspond across the population. The plan for the project week here is to figure out the best file format for storing this data, and adapting the modules accordingly.

The last stage of the pipeline takes the original meshes and the corresponding particle locations to create new meshes with corresponding vertex locations. The plan for the project week is to create a VTK filter for this task.

</div>

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

<h1>Progress</h1>

Slicer modules for each of the scan conversion, particle correspondence, and re-meshing stages are already created.

</div>

<br style="clear: both;" />

</div>

===References===
*Cortical Correspondence Using Entropy-Based Particle Systems and Local Features, Oguz I, Cates J, Fletcher T, Whitaker R, Cool D, Aylward S, Styner M, IEEE Symposium on Biomedical Imaging ISBI 2008, pp. 1637- 1640.
*J. Cates, T. Fletcher, and R. Whitaker, "Entropy-based particle systems for shape correspondence," Mathematical Foundations of Computational Anatomy Workshop, MICCAI 2006, pp. 90–99, Oct. 2006.
*J. Cates, T. Fletcher, M. Styner, M. Shenton, and R. Whitaker, “Shape modeling and analysis with entropy-based particle systems,” in IPMI, 2007, pp. 333–345.

2009 Winter Project Week Cortical Correspondence

2008-12-18T02:02:07Z

Ipek: New page: {| |Project Week Main Page ]] |[[Image:scarmri_namic.jpg|thumb|320px|Image showing scar in the left atrium using MR...

{|
|[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2009_Winter_Project_Week|Project Week Main Page]] ]]
|[[Image:scarmri_namic.jpg|thumb|320px|Image showing scar in the left atrium using MRI]]
|[[Image:scar_carto_namic.jpg|thumb|320px|Image of scar by MRI registered to MR Angiogram, and to the EP RF ablation site data.]]
|}

__NOTOC__

===Key Investigators===
* UNC: Ipek Oguz, Martin Styner
* Kitware: Will Schroeder
* GE: Xiaodong Tao

<div style="margin: 20px;">

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

<h1>Objective</h1>

We are developing a cortical correspondence framework that incorporates data from structural images as well as DTI connectivity. The goal at this project week is to create Slicer modules for the entire correspondence pipeline, where the input is surface meshes and local attributes at vertices, and the output is meshes with optimized vertex correspondence.

</div>

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

<h1>Approach, Plan</h1>

The correspondence pipeline consists of three stages. The first stage scan converts each input surface into a volume, and creates a signed distance map (the surface is the zero-level set of the distance map). It also interpolates the local attributes defined on the mesh vertices to obtain attribute volumes that match the distance volumes. The plan for the project week is to use the VTK point data reader/writer for the attributes.

The particle correspondence framework (the second stage) works on volumes containing distance maps to the surface, as well as attribute volumes. The output of the correspondence tool is a collection of particle locations defined on each surface, such that same index particles correspond across the population. The plan for the project week here is to figure out the best file format for storing this data, and adapting the modules accordingly.

The last stage of the pipeline takes the original meshes and the corresponding particle locations to create new meshes with corresponding vertex locations. The plan for the project week is to create a VTK filter for this task.

</div>

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

<h1>Progress</h1>

Slicer modules for each of the scan conversion, particle correspondence, and re-meshing stages are already created.

</div>

<br style="clear: both;" />

</div>

===References===
*Cortical Correspondence Using Entropy-Based Particle Systems and Local Features, Oguz I, Cates J, Fletcher T, Whitaker R, Cool D, Aylward S, Styner M, IEEE Symposium on Biomedical Imaging ISBI 2008, pp. 1637- 1640.

2009 Winter Project Week

2008-12-18T01:34:47Z

Ipek: /* Other NA-MIC Projects */

Back to [[Project Events]], [[AHM_2009]], [[Events]]

==Introduction to NA-MIC Project Week==

Please read an introduction about these events [[Project_Events#Introduction|here]].

== Dates.Venue.Registration ==

Please [[AHM_2009#Dates._Venue._Registration| click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2009#Agenda|click here for the agenda for AHM 2009 and Project Week]].

==Projects==
Please note:
*Please use the [[2009_Winter_Project_Week_Template|'''2009 Project Week Template''']] to create a page for your project(s)
*[[2008_Summer_Project_Week#Projects|Last Event's Projects as a reference]]
*For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]
*Next Project Week is at MIT -- June 22-26, 2009
The following is a list of all projects that will be pursued at this meeting.

===NA-MIC DBP Roadmap Projects===
Please note that these projects correspond to four clinical Roadmap application projects that will be pursued in focused parallel tracks at the meeting, each corresponding to a DBP problem.

#[[DBP2:Harvard:Brain_Segmentation_Roadmap|Harvard Roadmap Project: Stochastic Tractography for VCFS]]
##[[2009_Winter_Project_Week:GT_TubularSurfaceSeg|Tubular Surface Segmentation for fiber bundle extraction]] (Vandana Mohan GATech, Allen Tannenbaum GATech, Marek Kubicki BWH, Stephen Aylward Kitware)
##[[2009_Winter_Project_Week_StochasticTractography |Stochastic Tractograhy Tool for Slicer]] (Marek Kubicki BWH, Julien de Siebenthal BWH, Steve Pieper Isomics)
##[[2009_Winter_Project_Week_Slicer3Functioning |Evaluation of basic Slicer 3.0 Functionality from a User Perspective]] (Doug Terry BWH, Marek Kubicki BWH, Sylvain Bouix BWH, Steve Pieper, Wendy Plesniak, Nicole Aucoin)
##[[2009_Winter_Project_Week:DTIGroupAnalysis |Group analysis of DTI fiber bundles]] (Casey Goodlett, Guido Gerig, Marek Kubicki, Sylvain Bouix)
#[[DBP2:UNC:Cortical_Thickness_Roadmap|UNC Roadmap Project: Cortical Thickness Measurement for Autism]]
##[[2009_Winter_Project_Week:LocalCorticalThicknessPipeline|Local Cortical Thickness Pipeline]] (Clement Vachet, Martin Styner, Heather Cody Hazlett, Marc Niethammer, Ipek Oguz)
##[[2009_Winter_Project_Week:RegionalCorticalThicknessPipeline|Regional Cortical Thickness Pipeline]] (Cedric Mathieu, Clement Vachet, Martin Styner, Heather Cody Hazlett)
#[[DBP2:MIND:Roadmap|MIND Roadmap Project: Brain Lesion Analysis in Lupus]]
##[[2009_Winter_Project_Week:HighLevelWizard|Slicer High Level Wizard Project]](Steve Pieper, Mark Scully, Jeremy Bockholt)
##[[2009_Winter_Project_Week:LesionAlgorithms|Review of Lesion Algorithms]](Ross Whitaker, Vincent Magnotta, Marcel Prastawa, Mark Scully, Jeremy Bockholt)
##[[2009_Winter_Project_Week:LongitudinalLesions|Determine Requirements of Longitudinal Lesion Analyses]](Jeremy Bockholt, Marcel Prastawa, Mark Scully, Andriy Fedorov)
#[[DBP2:JHU:Roadmap|JHU Roadmap Project: Segmentation and Registration for Robotic Prostate Intervention]]
##[[2009_Winter_Project_Week:SterotacticFrameModel|Generating a Model of a Stereotactic Frame for Neurosurgery]] (Rares Crisan, Queens, Gabor Fichtinger, Queens, Katie Hayes, BWH)

===Other NA-MIC Projects===
#[[2009_Winter_Project_Week_Hageman_FMTractography | Fluid mechanics tractography and visualization]] (Nathan Hageman UCLA)
#UCLA BrainLab/Slicer Neurosurgery Preoperative Tumor Planning - using Slicer and its link to BrainLab to investigate whether different tractography methods aid in preoperative planning of tumor resection.(Nathan Hageman UCLA)
#Development of FEM / FVM solver library in ITK/VTK (and/or Python?) (Nathan Hageman UCLA, Vince, Luca, Steve)
#[[2009_Winter_Project_Week_Transform_Management | Transform Management]](Jim Miller)
#Interactive 3D Widgets - Introduce new interactors into Slicer; extensions to current widgets to support Slicer (Karthik, Will Schroeder, Nicole Aucoin)
#[[2009_Winter_Project_Week_vtkITK_Pipeline | Using ITK in VTK Pipelines]] (Jim, Steve)
#[[2009_Winter_Project_Week_SlicerLayouts | User Interface Flexible Layouts]] (Wendy, Jim, Steve, Sebastien, Ron)
#[[2009_Winter_Project_Week_Python | Python interface completion and packaging - Fortran and openssl problems]] (Luca, Steve, Demian)
#[[Two-tensor tractography in Slicer using Python and Teem]] (Madeleine Seeland, C-F Westin, Xiaodong Tao)
#[[2009_Winter_Project_Week_Rotation_Tangents | Diffusion Tensor Invariant gradients and rotation tangents in Python and Teem]] (Peter Savadjiev, C-F Westin, Luis Ibanez)
#Automated GUI testing (Sebastien, Interested User: Vince)
#[[2009_Winter_Project_Week_ColorModule | Slicer Colors Module update ]](Nicole)
#[[Volume Rendering]] (Alex, Curt, Nicholas)
#[[2009_Winter_Project_Week_XND | XNAT Desktop BatchMake integration & Slicer interface]] (Dan Marcus, Stephen Aylward, Wendy Plesniak, Curt Lisle)
#[[2009_Winter_Project_Week_Cortical_Correspondence | Cortical correspondence using DTI]] (Ipek, Martin, Xiaodong)
#[[2009_Winter_Project_Week_Command_Line_Program_Testing |Command Line Program Testing]] (Lorensen, Ron)
#[[2009_Winter_Project_Week_Slicer_VMTK |Vessel Segmentation in Slicer using VMTK]] ([[User:haehn|Daniel Haehn]], [[User:lantiga| Luca Antiga]])
#[[2009_Winter_Project_Week_fMRI_Clustering |Exploring Functional Connectivity in fMRI via Clustering]] (Archana Venkataraman, Marek Kubicki, Polina Golland)
#[[2009_Winter_Project_Week_Compiler_Warnings:Slicer3_Graffiti |Compiler Warnings:Slicer3's Graffiti]] (Lorensen, NA-MIC)
#[[2009_Winter_Project_Week_ChangeTracker |Measuring dynamics of tumor growth in Slicer3 with ChangeTracker]] (Andriy Fedorov)
#[[2009_Winter_Project_Week_GWE_Catalogs |GWE integration with catalog files]] (Marco)
#[[2009_Winter_Project_Week_Gofigure_LevelSet |ITK level set solution for cell segmentation in microscopy datasets]] (part of Gofigure) (Kishore mosaliganti)
#[[2009_Winter_Project_Week_Surface_Processing |ITK surface processing filters: Smoothing, spherical parameterization]] (part of Gofigure) (Alex. Gouaillard)
#[[2009_Winter_Project_Week_Manual_Segmentation_Widgets |VTK widgets for manual segmentation and manual validation of segmentation]] (part of Gofigure) (Arnaud Gelas)
#[[2009_Winter_Project_Week_UtahPlugins | Integration of Utah registration and segmentation methods as Slicer plugins]] (Marcel Prastawa)
#[[2009_Winter_Project_Week_OMTRegistration | DWI to MRI Registration Using Optimat Mass Transport]] (Sylvain Bouix, Ivan Kolesov GATech)

===External Collaborations===
* [[Iowa Meshing Tutorial]]
*Wake Forest - Virginia Tech
** [[2009_Winter_Project_Week_WFU1 | Move to All Slicer3 Workflow]]
** [[2009_Winter_Project_Week_WFU2 | Development of deformation based morphometry module]]
*Georgetown U: [[2009_Winter_Project_Week_NAVRFA | Prototype RF Lesion Ablation Workflow prototyped in Slicer]]
*UNC:
**[[2009_UNC_HAMMER | MR-image registration algorithm to be extended and added to namic kit]]
**[[2009_UNC_White_Matter_Lesion | white matter lesion segmentation]]
* [[Stanford SIMBIOS: Whole Body Segmentation for Simulation]]
* [[2009_Winter_Project_Week_MRSI | MRSI Module for Slicer (Bjoern Menze)]]
*[[NCI Evaluating NA-MIC Tools for Image Analysis]]

=== Preparation ===

# Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]
# Starting Thursday, October 16th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 16: Engineering Infrastructure Projects
#*October 23: Funded External Collaboration Projects
#*November 6: DPB Projects
#*November 20: New Collaborations
#*December 4: Other Projects
#*December 18: Loose Ends
#By December 17, 2008: [[2009_Winter_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 December 17, 2008: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
## 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. the BIRN). 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.)
## 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. (Zack)
# FINAL TCON: December 18th 3pm ET to tie loose ends
# 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...

== Previous Project Events ==

A history of all the programming/project events in NA-MIC is available by following [[Project Events|this link]].

2009 Winter Project Week

2008-10-30T19:55:40Z

Ipek: /* Other NA-MIC Projects */

Back to [[Project Events]], [[AHM_2009]], [[Events]]
__NOTOC__

==Introduction to NA-MIC Project Week==

Please read an introduction about these events [[Project_Events#Introduction|here]].

== Dates.Venue.Registration ==

Please [[AHM_2009#Dates._Venue._Registration| click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2009#Agenda|click here for the agenda for AHM 2009 and Project Week]].

==Projects==
Please note:
*Please use the [[2009_Winter_Project_Week_Template|'''2009 Project Week Template''']] to create a page for your project(s)
*[[2008_Summer_Project_Week#Projects|Last Event's Projects as a reference]]
*For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]
*Next Project Week is at MIT -- June 22-26, 2009
The following is a list of all projects that will be pursued at this meeting.

===NA-MIC DBP Roadmap Projects===
Please note that these projects correspond to four clinical Roadmap application projects that will be pursued in focused parallel tracks at the meeting, each corresponding to a DBP problem.

#[[DBP2:Harvard:Brain_Segmentation_Roadmap|Harvard Roadmap Project: Stochastic Tractography for VCFS]]
#[[DBP2:UNC:Cortical_Thickness_Roadmap|UNC Roadmap Project: Cortical Thickness Measurement for Autism]]
#[[DBP2:MIND:Roadmap|MIND Roadmap Project: Brain Lesion Analysis in Lupus]]
#[[DBP2:JHU:Roadmap|JHU Roadmap Project: Segmentation and Registration for Robotic Prostate Intervention]]

===Other NA-MIC Projects===
#Fluid mechanics tractography and visualization (Nathan Hageman UCLA)
#UCLA BrainLab/Slicer Neurosurgery Preoperative Tumor Planning - using Slicer and its link to BrainLab to investigate whether different tractography methods aid in preoperative planning of tumor resection.(Nathan Hageman UCLA)
#Development of FEM / FVM solver library in ITK/VTK (and/or Python?) (Nathan Hageman UCLA, Vince, Luca, Steve)
#Transform Management(Jim Miller)
#Interactive 3D Widgets - Introduce new interactors into Slicer (Karthik)
#vtkITK Pipeline (Jim, Steve)
#User Interface Flexible Layouts (Wendy, Jim, Steve)
#Packaging Python Interface - Fortran and openssl problems(Luca, Steve)
#xnat and batchmake integration (Julien, Dan Marcus)
#Automated GUI testing (Sebastien, Interested User: Vince)
#Slicer Colors Module update (Nicole)
#Volume Rendering (Alex, Curt)
#XNAT Dekstop & File Repository prototypes (Dan Marcus)
#Cortical correspondence using DTI (Ipek, Martin)

===External Collaborations===
*Iowa
*Wake Forest - Virginia Tech
** [[2009_Winter_Project_Week_WFU1 | Move to All Slicer3 Workflow]]
** [[2009_Winter_Project_Week_WFU2 | Development of deformation based morphometry module]]
*Georgetown U.
*UNC
*Mario Negri

=== Preparation ===

# Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]
# Starting Thursday, October 16th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 16: Engineering Infrastructure Projects
#*October 23: Funded External Collaboration Projects
#*October 30: DPB Projects (2)
#*November 6: DPB Projects (2)
#*November 13: New Collaborations
#*November 20: New Collaborations
#*December 4: Other Projects
#*December 18: Loose Ends
#By December 17, 2008: [[2009_Winter_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 December 17, 2008: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
## 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. the BIRN). 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.)
## 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. (Zack)
# FINAL TCON: December 18th 3pm ET to tie loose ends
# 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...

== Previous Project Events ==

A history of all the programming/project events in NA-MIC is available by following [[Project Events|this link]].

Projects:CorticalCorrespondenceWithParticleSystem

2008-08-28T02:08:08Z

Ipek: /* Description */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:UNC|UNC Algorithms]], [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Cortical Correspondence with Particle Systems =

[[Image:Sulcaldepth.png|thumb|300px|]]

In this project, we want to compute cortical correspondence on populations,
using various features such as cortical structure, DTI connectivity, vascular
structure, and functional data (fMRI). This presents a challenge because of
the highly convoluted surface of the cortex, as well as because of the different
properties of the data features we want to incorporate together.

= Description =

We would like to use a particle based entropy minimizing system for the correspondence
computation, in a population-based manner. This method best suits
our needs since, parametrization based methods, such as MDL or SPHARM,
require a spherical parametrization of the surface, which is hard to obtain for
the highly convoluted cortex surface. Another advantage of using the particle
based correspondence technique is that it does not require the surface to be of
spherical topology; this means a lot less pre-processing for our method, since
the brain cortex is not of spherical topology. Another strength of this method is
that it would (eventually) enable correspondence computation on the subcortical structures
and on the cortical surface using the same framework. We also would like to
explore correspondence on the cerebellum, which traditionally is excluded from
such studies (e.g. in FreeSurfer-based work).

The main disadvantage of using the particle based correspondence technique
on the brain cortex is that it assumes the particles to be existing on local tangent
planes, which presents a challenge for the cortex given the highly convoluted
surface. We are hoping to overcome this difficulty by first ‘inflating’ the cortex
surface. This way, we obtain a less convoluted, sphere-like surface, where the
particles will be interacting. However, we need a 1-1 correspondence between
the original cortex surface and the inflated surface, since the data to be used
for correspondence, such as the the curvature, and the vascular data, lives on
the original cortex surface. FreeSurfer offers a method that minimizes the distance distortion in the
mapping, while also smoothing the surface. FreeSurfer also preprocesses the
input surface to generate a spherical topology.

Once the framework for computing the correspondence given certain data features
is established, the major challenge is to incorporate the various data forms
that we would like to use together. We are planning on using structural data as well as
connectivity (DTI). So far, we have tested our method using structural metrics, namely, sulcal depth (as computed by FreeSurfer), and have demonstrated improved correspondence quality compared to traditional, location-only correspondence (using the particle-based entropy framework), and we have shown that our results are at least comparable to FreeSurfer. This comes as no surprise as we had already shown in [[Projects:PopulationBasedCorrespondence|our previous studies]] that correspondence can be enhanced by using local curvature in addition of point locations for objects with complex geometry. Currently, we are at
testing stage using connectivity information obtained with stochastic tractography methods developed as part of a [[Projects:DTIStochasticTractography | separate NA-MIC project]]. It remains to be seen whether this information can be used to further improve cortical correspondence.

= Publications =
*[http://www.na-mic.org/pages/Special:PubDB_View?dspaceid=1444 Oguz I, Cates J, Fletcher T, Whitaker R, Cool D, Aylward S, Styner M. Cortical correspondence using entropy-based particle systems and local features. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1637-1640.]
* [http://www.na-mic.org/pages/Special:Publications?text=Projects%3ACorticalCorrespondenceWithParticleSystem&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| Cates J, Fletcher P, Styner M, Shenton M, Whitaker R. Shape modeling and analysis with entropy-based particle systems. Inf Process Med Imaging. 2007;20:333-45.]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Martin Styner
* Utah Algorithms: Josh Cates, Tom Fletcher, Ross Whitaker

[[Category:fMRI]] [[Category:Shape Analysis]] [[Category: Diffusion MRI]]

Projects:PopulationBasedCorrespondence

2008-08-28T01:58:14Z

Ipek: /* Publications */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:UNC|UNC Algorithms]]
__NOTOC__
= Correspondence of complex structures using (Curvature + Location) MDL =

[[Image:UNCShape_ShapeCorrespondence.png|thumb|300px|]]

The SPHARM-PDM based correspondence is a global correspondence method that does performs well for many structures. But in our studies it has shown to be inferior to population based correspondence methods, when assessing statistical modeling properties derived from the established correspondence, such as specificity and generalization ability of a statistical model. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment.

= Description =

We have extended the population based correspondence framework to include curvature based measurements, such as the Koenderink '''Shape''' Index S and Curvedness C in combination with the standard location information. The implementation is based on ITK and uses the SPHARM-PDM correspondence as an initialization. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach. Especially with more complex structures, such as the femural bone and the striatal structure (composed of caudate, nucleus accumbens and putamen), our method outperforms the other methods. It also illustrates the potential of this approach of objects as complex as the human cortex, the object of study in the NAMIC year 07/08.

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Martin Styner

= Publications =

* [http://www.na-mic.org/pages/Special:PubDB_View?dspaceid=1443 Styner M, Oguz I, Heimann T, Gerig G. Minimum description length with local geometry. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1283-1286.]
* [http://www.na-mic.org/pages/Special:PubDB_View?dspaceid=1444 Oguz I, Cates J, Fletcher T, Whitaker R, Cool D, Aylward S, Styner M. Cortical correspondence using entropy-based particle systems and local features. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1637-1640.]

= Links =

* [[NA-MIC/Projects/Structural/Shape_Analysis/Correspondence|Shape Correspondence Based on Local Curvature]]

Project Week Results: [[media:2006_MIT_Project_Week_LocalCurvatureBasedCorrespondence.ppt|Jun 2006]]

Projects:CorticalCorrespondenceWithParticleSystem

2008-08-28T01:55:16Z

Ipek: /* Publications */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:UNC|UNC Algorithms]], [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Cortical Correspondence with Particle Systems =

[[Image:Sulcaldepth.png|thumb|300px|]]

In this project, we want to compute cortical correspondence on populations,
using various features such as cortical structure, DTI connectivity, vascular
structure, and functional data (fMRI). This presents a challenge because of
the highly convoluted surface of the cortex, as well as because of the different
properties of the data features we want to incorporate together.

= Description =

We would like to use a particle based entropy minimizing system for the correspondence
computation, in a population-based manner. This method best suits
our needs since, parametrization based methods, such as MDL or SPHARM,
require a spherical parametrization of the surface, which is hard to obtain for
the highly convoluted cortex surface. Another advantage of using the particle
based correspondence technique is that it does not require the surface to be of
spherical topology; this means a lot less pre-processing for our method, since
the brain cortex is not of spherical topology. Another strength of this method is
that it would (eventually) enable correspondence computation on the subcortical structures
and on the cortical surface using the same framework. We also would like to
explore correspondence on the cerebellum, which traditionally is excluded from
such studies (e.g. in FreeSurfer-based work).

The main disadvantage of using the particle based correspondence technique
on the brain cortex is that it assumes the particles to be existing on local tangent
planes, which presents a challenge for the cortex given the highly convoluted
surface. We are hoping to overcome this difficulty by first ‘inflating’ the cortex
surface. This way, we obtain a less convoluted, sphere-like surface, where the
particles will be interacting. However, we need a 1-1 correspondence between
the original cortex surface and the inflated surface, since the data to be used
for correspondence, such as the the curvature, and the vascular data, lives on
the original cortex surface. FreeSurfer offers a method that minimizes the distance distortion in the
mapping, while also smoothing the surface. FreeSurfer also preprocesses the
input surface to generate a spherical topology.

Once the framework for computing the correspondence given certain data features
is established, the major challenge is to incorporate the various data forms
that we would like to use together. We are planning on using structural,
functional(fMRI) and vascular data, as well as connectivity (DTI). Currently, we are at
testing stage using structural data, namely, point locations and sulcal depth (as computed by
FreeSurfer). We know from [[Projects:PopulationBasedCorrespondence|our previous studies]] that
correspondence can be enhanced by using local curvature in addition of point locations for
objects with complex geometry. It remains to be seen whether this is the case for the cortex.

= Publications =
*[http://www.na-mic.org/pages/Special:PubDB_View?dspaceid=1444 Oguz I, Cates J, Fletcher T, Whitaker R, Cool D, Aylward S, Styner M. Cortical correspondence using entropy-based particle systems and local features. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1637-1640.]
* [http://www.na-mic.org/pages/Special:Publications?text=Projects%3ACorticalCorrespondenceWithParticleSystem&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| Cates J, Fletcher P, Styner M, Shenton M, Whitaker R. Shape modeling and analysis with entropy-based particle systems. Inf Process Med Imaging. 2007;20:333-45.]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Martin Styner
* Utah Algorithms: Josh Cates, Tom Fletcher, Ross Whitaker

[[Category:fMRI]] [[Category:Shape Analysis]] [[Category: Diffusion MRI]]