NAMIC Wiki - User contributions [en]

2013 Summer Project Week

2013-06-21T13:33:16Z

Kfritscher76: /* Radiation Therapy */

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

<gallery widths="800px" heights="300px" perrow="1" caption="Lunch in the Amphitheater at Stata, June 20 2013">
Image:Lunch at Project week IMG 0367.JPG|Shade was a popular commodity
</gallery>

Dates: June 17-21, 2013.

Location: MIT, Cambridge, MA.

==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, June 17
!style="width:18%" |Tuesday, June 18
!style="width:18%" |Wednesday, June 19
!style="width:18%" |Thursday, June 20
!style="width:18%" |Friday, June 21
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"|'''NA-MIC Update Day'''
|
|bgcolor="#88aaae"|'''IGT and RT Day'''
|bgcolor="#faedb6"|'''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9am-12pm'''
|
|'''10-11am''' [[2013 Project Week Breakout Session:Slicer4Python|Slicer4 Python Modules, Testing, Q&A]] (Steve Pieper) <br>
[[MIT_Project_Week_Rooms|Grier Room (Left)]]
|'''9:30-11pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2013 Project Week Breakout Session: SimpleITK|Slicer and SimpleITK]] (Hans Johnson, Brad Lowekamp)
[[MIT_Project_Week_Rooms#32-D507|32-D507]]
|
'''9:30-10:30am''' [[2013_Tutorial_Contest|Tutorial Contest Presentations]] <br>
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''10am-12pm: <font color="#4020ff">Breakout Session:'''</font><br>[[2013 Project Week Breakout Session: IGT|Image-Guided Therapy]] (Tina Kapur)
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
|'''10am-12pm:''' [[#Projects|Project Progress Updates]] <br>
'''12pm''' [[Events:TutorialContestJune2013|Tutorial Contest Winner Announcement]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|-
|bgcolor="#ffffdd"|'''12pm-1pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|[http://www.na-mic.org/Wiki/index.php/File:NamicToAmphi.png Lunch in Outdoor Amphitheather!]
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1pm-5:30pm'''
|'''1-1:05pm: <font color="#503020">Ron Kikinis: Welcome</font>'''
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3:30-4:30pm''' [[2013 Summer Project Week Breakout Session:SlicerExtensions|Slicer4 Extensions]] (Jean-Christophe Fillion-Robin) <br>
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Room (Left)]]
|'''1-3pm:''' [[Renewal-06-2013|NA-MIC Renewal]] <br>PIs <br>Closed Door Session with Ron
[[MIT_Project_Week_Rooms#32-D407|32-262]]
|
|<br>----------------------------------------<br>'''3-5:30pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2013 Summer Project Week Breakout Session:RT|Radiation Therapy]] (Greg, Csaba)
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
|
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|
|}

== '''Projects''' ==

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

==Some Stats==
73 Projects, 104 Attendees. First time attendees 25.

===Huntington's Disease===
* [[Learn and Apply FiberBundleLabelSelect for Huntington's Disease Data]] (Hans, Demian)
* [[Investigate Potential Tensor Computation Improvement via Positive Semi-Definite (PSD) Tensor Estimation]] (Hans)
* [[2013_Summer_Project_Week:SinglePrecisionRegistrationITK| Single Precision Registration]] (Hans, Brad Lowekamp, Dave, Ali Ghayoor)
* [[Dynamically Configurable Quality Assurance Module for Large Huntington's Disease Database Frontend]] (Dave)
* [[DWIConvert]] (Dave, Kent Williams)
* [[Enhance and update SPL atlas]] (Dave, Hans)
* [[Linear Mixed-effects shape model to explore Huntington's Disease Data]] (Manasi, Dave, Josh, Hans, Ross)


7 projects

===Traumatic Brain Injury===
* [[Visualization and quantification of peri-contusional white matter bundles in traumatic brain injury using diffusion tensor imaging]] (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)
* [[Clinically oriented assessment of local changes in the properties of white matter affected by intra-cranial hemorrhage]] (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)
* [[Validation and testing of 3D Slicer modules implementing the Utah segmentation algorithm for traumatic brain injury]] (Bo Wang, Marcel Prastawa, Andrei Irimia, Micah Chambers, Guido Gerig, Jack van Horn)
* [[Exploring multi-modal registration for improved longitudinal modeling of patient-specific 4D DTI data]] (Anuja Sharma, Bo Wang, Andrei Irimia, Micah Chambers, Guido Gerig, Jack van Horn)
* [[2013_Summer_Project_Week: A Portable Ultrasound Device for Intracranial Hemorrhage Detection|A Portable Ultrasound Device for Intracranial Hemorrhage Detection]] (Jason White, Vicki Noble, Kirby Vosburgh)


5 projects

===Atrial Fibrillation and Cardiac Image Analysis===
* [[2013_Summer_Project_Week:Fibrosis_analysis|Fibrosis distribution analysis]] (Yi Gao, LiangJia Zhu, Josh Cates, Rob MacLeod, Sylvain Bouix, Ron Kikinis, Allen Tannenbaum)
* [[2013_Summer_Project_Week:SegmentationAidedRegistration|Segmentation Aided Registration]] (Yi Gao, LiangJia Zhu, Josh Cates, Rob MacLeod, Sylvain Bouix, Ron Kikinis, Allen Tannenbaum)
* [[2013_Summer_Project_Week:CARMA_workflow_wizard|Cardiac MRI Toolkit LA segmentation and enhancement quantification workflow wizard]] (Salma Bengali, Alan Morris, Brian Zenger, Josh Cates, Rob MacLeod)
* [[2013_Summer_Project_Week:CARMA_Documentataion|Cardiac MRI Toolkit Documentation Project]] (Salma Bengali, Alan Morris, Brian Zenger, Josh Cates, Rob MacLeod)
* [[2013_Summer_Project_Week:CARMA_Visualization|LA model visualization]] (Salma Bengali, Alan Morris, Josh Cates, Rob MacLeod)
* [[2013_Summer_Project_Week:CARMA_AutoLASeg|Cardiac MRI Toolkit: Automatic LA Segmentation with Graph Cuts Module]] (Salma Bengali, Alan Morris, Josh Cates, Gopal, Ross Whitaker, Rob MacLeod)
* [[2013_Summer_Project_Week:Sobolev_Segmenter|Medical Volume Segmentation Using Sobolev Active Contours]] (Arie Nakhmani, Yi Gao, LiangJia Zhu, Rob MacLeod, Josh Cates, Ron Kikinis, Allen Tannenbaum)
* [[2013_Summer_Project_Week:Left_Ventricle_Motion_Analysis_using_Tagged_MRI|Left Ventricle Motion Analysis using Tagged MRI]] (Yang Yu, Shaoting Zhang, Dimitris Metaxas)


8 projects

===Radiation Therapy===
* [[2013_Summer_Project_Week:Landmark_Registration| Landmark Registration]] (Steve, Nadya, Greg, Paolo, Erol)
* [[2013_Summer_Project_Week:Slicer_RT:_DICOM-RT_Export | SlicerRT: Dicom-RT Export]] (Greg Sharp, Kevin Wang, Csaba Pinter)
* [[2013_Summer_Project_Week:Proton_dose_calculation | Proton dose calculation]] (Greg Sharp, Kevin Wang, Maxime Desplanques)
* [[2013_Summer_Project_Week:Deformable_registration_validation_toolkit | Deformable registration validation toolkit]] (Greg Sharp, Kevin Wang, Andrey Fedorov)
* [[2013_Summer_Project_Week:Deformable_transforms | Deformable transform handling in Transforms module]] (Csaba Pinter, Alex Yarmarkovich, Andras Lasso)
* [[2013_Summer_Project_Week:Brachy_HDR_Ultrasound | Creating DICOM series from ultrasound for prostate HDR brachytherapy]] (Adam Rankin)
* [[Analysis of different atlas-based segmentation techniques for parotid glands]] (Karl Fritscher, Christian Wachinger, Greg Sharp, Matthew Brennan)
* [[2013_Summer_Project_Week:Prostate_radiotherapy_DICOM_communication|DICOM communication between Slicer and Oncentra Prostate radiation therapy planning system]] (Andrey Fedorov, Csaba Pinter, Emily Neubauer-Sugar, Alireza Mehrtash)


8 projects

===IGT and Device Integration with Slicer===
* [[2013_Summer_Project_Week:TractAtlasCluster| Tract Atlas and Clustering for Neurosurgery]] (Lauren O'Donnell)
* [[2013_Summer_Project_Week:SlicerIGT_Extension| SlicerIGT extension]] (Tamas Ungi, Junichi Tokuda, Laurent Chauvin)
* [[2013_Summer_Project_Week:Ultrasound_Calibration| Ultrasound Calibration]] (Matthew Toews, Daniel Kostro, William Wells, Stephen Aylward, Isaiah Norton, Tamas Ungi)
* [[2013_Summer_Project_Week:Ultrasound_Needle_Detection| Ultrasound Needle Detection]] (Alireza Mehrtash, Daniel Kostro, Matthew Toews, Tamas Ungi, William Wells, Tina Kapur)
* [[2013_Summer_Project_Week:Liver_Trajectory_Management| Liver Trajectory Management]] (Laurent Chauvin, Junichi Tokuda)
* [[2013_Summer_Project_Week:LabelMapStatistics| Label map statistics]] (Laurent Chauvin, Csaba Pinter)
* [[2013_Summer_Project_Week:PerkTutorExtension| Perk Tutor Extension]] (Matthew Holden, Tamas Ungi)
* [[2013_Summer_Project_Week:Open_source_electromagnetic_trackers_using_OpenIGTLink| Open-source electromagnetic trackers using OpenIGTLink]] (Peter Traneus Anderson, Tina Kapur, Sonia Pujol)
* [[2013_Summer_Project_Week:3D_prostate_segmentation_of_Ultrasound_image| Segmentation of Prostate Gland from 3D US for Prostate Interventions]] (Xu Li, Andriy Fedorov, Tina Kapur, William Wells)
* [[2013_Summer_Project_Week:3D_prostate_registration_of_Ultrasound_image_using_MRI| Registration of an MRI delineated image of the Prostate Gland ot a 3D US for Prostate Interventions]] (Demian Wassermann, Andriy Fedorov, Tina Kapur, William Wells)
* [[2013_Summer_Project_Week:ProstateBRP| MRI-guided robotic prostate interventinos]] (Junichi Tokuda, Greg Fischer, Nirav Patel, Nobuhiko Hata, Clare Tempnay)


12 projects

===Chronic Obstructive Pulmonary Disease===
* [[2013_Summer_Project_Week:Airway_Inspector_Porting | Porting Airway Inspector to Slicer 4]] (Raul San Jose, Demian Wassermann, Rola Harmouche)
* [[2013_Summer_Project_Week:MRML_Infrastructure_Airway_Inspector | Airway Inspector: Slicer Extension and MRML Infrastructure]] (Demian Wassermann, Raul San Jose, Rola Harmouche)
* [[2013_Summer_Project_Week:Nipype_CLI_Integration | Integration of Nipype with CLI modules in the Chest Imaging Platform Library ]] (Rola Harmouche,Demian Wassermann, Raul San Jose)


3 projects

=== '''Additional Collaborations'''===
* [[2013_Summer_Project_Week:VicomTech| VicomTech visit: Imaging solutions and Slicer use]] (Ivan Macia)
* [[2013_Summer_Project_Week:Radnostics |Spine Segmentation & Osteoporosis Detection In CT Imaging Studies]] (Anthony Blumfield, Ron Kikinis)
* [[2013_Summer_Project_Week: Computer Assisted Surgery| Computer Assisted Reconstruction of Complex Bone Fractures]] (Karl Fritscher, Peter Karasev, Ivan Kolesov, Allen Tannenbaum, Ron Kikinis)
* [[2013_Summer_Project_Week:XNAT 3D Viewer| XNAT 3D Viewer]] (Amanda Hartung, Steve Pieper, Daniel Haehn)
* [[2013_Summer_Project_Week:kukarobot| Interface for the integration of a KUKA robot using OpenIGTLink]] (Sebastian Tauscher, Thomas Neff, Junichi Tokuda, Nobuhiko Hata)
* [[2013_Summer_Project_Week:Application of Statistical Shape Modeling to Robot Assisted Spine Surgery | Application of Statistical Shape Modeling to Robot Assisted Spine Surgery]] (Marine Clogenson)
* [[2013_Summer_Project_Week:Robot_Control| Robot Control]] (A.Vilchis, J-C. Avila-Vilchis, S.Pujol)
* [[2013_Summer_Project_Week:Epilepsy_Surgery|Identification of MRI Blurring in Temporal Lobe Epilepsy Surgery]] (Luiz Murta)
* [[2013_Summer_Project_Week:_Is_Neurosurgical_Rigid_Registration_Really_Rigid%3F| Is Neurosurgical Rigid Registration Really Rigid?]] (Athena)
* [[2013_Summer_Project_Week: Individualized Neuroimaging Content Analysis using 3D Slicer in Alzheimer's Disease| Individualized Neuroimaging Content Analysis using 3D Slicer]] (Sidong Liu, Weidong Cai, Sonia Pujol, Ron Kikinis)
* [[2013_Summer_Project_Week:CMFReg | Cranio-Maxillofacial Registration]] (Francois Budin, Vinicius Boen)
* [[2013_Summer_Project_Week:DTIPipelineExtensions | DTI Analysis Pipeline as Slicer4 Extensions]] (Francois Budin)
*[[2013_Project_Week:WebbasedAnatomicalTeachingFrameworkSummer2013|Web-based anatomical teaching framework]] (Lilla Zollei, Nathaniel Reynolds, Daniel Haehn, Nicolas Rannou, Steve Pieper, Rudolph Pienaar)
* [[2013_Summer_Project_Week:Biomedical_Image_Computing_Teaching_Modules|3D Slicer based Biomedical image computing teaching modules]] (A. Vilchis, J-C. Avila-Vilchis, S. Pujol)
* [[2013_Summer_Project_Week:Analyzing Breast Tumor Heterogeneity Using 3D Slicer|Analyzing Breast Tumor Heterogeneity Using 3D Slicer]] (Sneha Durgapal, Jayender Jagadeesan, Tobias Penzkofer, Vivek Narayan)
* [[2013_Summer_Project_Week:WMH Segmentation for Stroke|WMH Segmentation for Stroke]] (Adrian Dalca, Ramesh Sridharan, Kayhan Batmanghelich, Polina Golland)
* [[2013_Summer_Project_Week:WMQL Integration in Slicer|White Matter Query Language (WMQL) Integration in Slicer]] (Demian Wassermann, Carl-Fredrik Westin)
* [[2013_Summer_Project_Week:Documentation for Slicer 4.2 Data Loading and 3D Visualization|Documentation for Slicer 4.2 Data Loading and 3D Visualization]] (Parth Amin WIT, Matt Flynn WIT, Dr. Pujol HMS)
* [[2013_Summer_Project_Week:WM Geometry Tools|Integration in Slicer of white matter geometry tools]] (Peter Savadjiev, Carl-Fredrik Westin)


16 projects

==='''Infrastructure'''===
* [[2013_Summer_Project_Week:MarkupsModuleSummer2013| Markups/Annotations rewrite]] (Nicole Aucoin, Ron Kikinis)
* [[2013_Summer_Project_Week:Patient_hierarchy | Patient hierarchy]] (Csaba Pinter, Andras Lasso, Steve Pieper)
* [[2013_Summer_Project_Week:CLI_Improvements | CLI Improvements (hierarchy nodes, related nodes, roles)]] (Andras Lasso, Csaba Pinter, Jc, Steve, Jim)
* [[2013_Summer_Project_Week:CLI_Matlab | CLI module implementation in Matlab]] (Andras Lasso, Jc, Steve, Jim)
* [[2013_Summer_Project_Week:Sample_Data | Sample Data]] (Steve Pieper, Jim Miller, Bill Lorensen, Jc)
* [[ITK -VTK wrapper]] (Paolo Zaffino, Greg Sharp, Steve Pieper)
* [[2013_Summer_Project_Week:Optimizing start time of slicer| Optimizing start time of slicer]] (Jc, Steve)
* [[Common resampling and conversion utility functions in Slicer]] (Csaba Pinter, Steve Pieper, Hans, Kevin Wang)
* [[2013_Summer_Project_Week:CLI_modules_in_MeVisLab| Integrating CTK CLI modules into MeVisLab]] (Hans Meine, Steve, Jc)
* [[2013_Summer_Project_Week:SimpleITK_GUI| Common Usage GUI for SimpleITK]] (Brad Lowekamp, Hans Johnson, Dave Welch, Steve)
* [[2013_Summer_Project_Week:Extension_dependencies| Extension dependencies]] (Jc, Adam Rankin)
* [[2013_Summer_Project_Week:Parameter_heirarchy| Parameter (time) Hierarchy]] (Adam Rankin)
* [[2013_Summer_Project_Week:ITK_Shared_IO_Libraries| ITK Shared I/O Libraries]] (Brad Lowekamp, Jc, Jim)
* [[2013_Summer_Project_Week:Python_Embedding| Python Embedding Library]] (Rui, Jim, Jc, Steve)

14 projects

== '''Background''' ==

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

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

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

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

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

== '''Logistics''' ==

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

== '''Preparation''' ==

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

== '''Registrants''' ==

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

#Parth Amin, WIT, aminp@wit.edu
#Charles Anderson, BWH, canderson26@partners.org
#Peter Anderson, retired, traneus@verizon.net
#Nicole Aucoin, BWH, nicole@bwh.harvard.edu
#Juan Carlos Avila Vilchis, Univ del Estado de Mexico, jc.avila.vilchis@hotmail.com
#Kayhan Batmanghelich, MIT, kayhan@csail.mit.edu
#Salma Bengali, Univ UT, salma.bengali@carma.utah.edu
#Anthony Blumfield, Radnostics, Anthony.Blumfield@Radnostics.com
#Vinicius Boen, Univ Michigan, vboen@umich.edu
#Taylor Braun-Jones, GE Healthcare, taylor.braun-jones@ge.com
#Matthew Brennan, MIT, brennanm@mit.edu
#Francois Budin, NIRAL-UNC, fbudin@unc.edu
#Ivan Buzurovic, BWH/HMS, ibuzurovic@lroc.harvard.edu
#Josh Cates, Univ UT, cates@sci.utah.edu
#Micah Chambers, UCLA, micahcc@ucla.edu
#Laurent Chauvin, BWH - SPL, lchauvin@bwh.harvard.edu
#Marine Clogenson, Ecole Polytechnique Federale de Lausanne (Switzerland), marine.clogenson@epfl.ch
#Adrian Dalca, MIT, adalca@MIT.EDU
#Matthew D'Artista, BWH - SPL, mdartista7@gmail.com
#Manasi Datar, Univ UT-SCI Institute, datar@sci.utah.edu
#Sneha Durgapal, BWH, durgapalsneha@gmail.com
#Luping Fang, Zhejiang Univ of Technology (China), flp@zjut.edu.cn
#Andriy Fedorov, BWH, fedorov@bwh.harvard.edu
#Jean-Christophe Fillion-Robin, Kitware, jchris.fillionr@kitware.com
#Gregory Fischer, WPI, gfischer@wpi.edu
#Barton Fiske, zSpace Inc, bfiske@zspace.com
#Matthew Flynn, WIT, flynnm3@wit.edu
#Karl Fritscher, MGH, kfritscher@gmail.com
#Yi Gao, Univ AL Birmingham, gaoyi.cn@gmail.com
#Alexandra Golby, BWH, agolby@partners.org
#Maria Gonzalez-Puente, WIT, gonzalezpuentem@wit.edu
#Dan Groszmann, GE Healthcare, daniel.groszmann@ge.com
#Daniel Haehn, Boston Childrens Hospital, daniel.haehn@childrens.harvard.edu
#Michael Halle, BWH-SPL, mhalle@bwh.harvard.edu
#Rola Harmouche, BWH, rharmo@bwh.harvard.edu
#Amanda Hartung, Rochester Inst of Tech, amh1646@rit.edu
#Nobuhiko Hata, BWH, hata@bwh.harvard.edu
#Nicholas Herlambang, AZE Technology Inc, nicholas.herlambang@azetech.com
#Matthew Holden, Queen's Univ (Canada), mholden8@cs.queensu.ca
#Andrei Irimia, UCLA, andrei.irimia@loni.ucla.edu
#Jayender Jagadeesan, BWH-SPL, jayender@bwh.harvard.edu
#Sylvain Jaume, WIT, jaumes@wit.edu
#Daniel Jimenez, daniel.jimenez@gmail.com
#Hans Johnson, Univ Iowa, hans-johnson@uiowa.edu
#Tina Kapur, BWH/HMS, tkapur@bwh.harvard.edu
#Alex Kikinis, BWH, alexkikinis@gmail.com
#Ron Kikinis, HMS, kikinis@bwh.harvard.edu
#Nils Klarlund, IEEE, klarlund@ieee.org
#Daniel Kostro, BWH, dkostro@bwh.harvard.edu
#Andras Lasso, Queen's Univ (Canada), lasso@cs.queensu.ca
#Rui Li, GE Global Research, li.rui@ge.com
#Xu Li, BWH, lixu0103@gmail.com
#Lichen Liang, MGH, lichenl@nmr.mgh.harvard.edu
#Sidong Liu, Univ Sydney (Australia), sliu7418@uni.sydney.edu.au
#William Lorensen, Bill's Basement, bill.lorensen@gmail.com
#Bradley Lowekamp, Medical Science & Computing Inc, bradley.lowekamp@nih.gov
#Athena Lyons, Univ Western Australia, 20359511@student.uwa.edu.au
#Nikos Makris, MGH, nikos@nmr.mgh.harvard.edu
#Katie Mastrogiacomo, BWH - SPL, kmast@bwh.harvard.edu
#Alireza Mehrtash, BWH - SPL, mehrtash@bwh.harvard.edu
#Hans Meine, Fraunhofer MEVIS (Germany), hans.meine@mevis.fraunhofer.de
#Jim Miller, GE Global Research, millerjv@ge.com
#Luis Murta, Univ Sao Paulo (Brazil), lomurta@gmail.com
#Arie Nakhmani, Univ AL Birmingham, anry@uab.edu
#Isaiah Norton, BWH, inorton@bwh.harvard.edu
#Lauren O'Donnell, BWH, odonnell@bwh.harvard.edu
#Dirk Padfield, GE Global Research, padfield@research.ge.com
#Jian Pan, Zhejiang Univ of Technology (China), pj@zjut.edu.cn
#George Papadimitriou, MGH, georgep@nmr.mgh.harvard.edu
#Nirav Patel, WPI, napatel@wpi.edu
#Tobias Penzkofer, BWH - SPL, pt@bwh.harvard.edu
#Rudolph Pienaar, Boston Childrens Hospital, Rudolph.Pienaar@childrens.harvard.edu
#Steve Pieper, Isomics Inc, pieper@isomics.com
#Csaba Pinter, Queen's Univ (Canada), pinter@cs.queensu.ca
#William Plishker, IGI Technologies, will@igitechnologies.com
#Sonia Pujol, HMS, spujol@bwh.harvard.edu
#Adam Rankin, Queen's Univ (Canada), rankin@cs.queensu.ca
#Nathaniel Reynolds, MGH, reynolds@nmr.mgh.harvard.edu
#Raul San Jose, BWH, rjosest@bwh.harvard.edu
#Peter Savadjiev, BWH, petersv@bwh.harvard.edu
#Tobias Schroeder, GE Healthcare, tobias.schroeder@ge.com
#Anuja Sharma, Univ UT-SCI Institute, anuja@cs.utah.edu
#Greg Sharp, MGH, gcsharp@partners.org
#Nadya Shusharina, MGH, nshusharina@partners.org
#Sebastian Tauscher, Leibniz Univ Hannover (Germany), sebastian.tauscher@imes.uni-hannover.de
#Clare Tempany, BWH, ctempanyafdhal@partners.org
#Cyrill von Tiesenhausen, KUKA Laboratories (Germany), cyrill.tiesenhausen@kuka.com
#Gaurie Tilak, BWH, gaurie_tilak@hms.harvard.edu
#Matthew Toews, BWH/HMS, mt@bwh.harvard.edu
#Junichi Tokuda, BWH, tokuda@bwh.harvard.edu
#Tamas Ungi, Queen's Univ (Canada), ungi@cs.queensu.ca
#Adriana Vilchis González, Univ del Estado de Mexico, hvigady@hotmail.com
#Kirby Vosburgh, BWH, kirby@bwh.harvard.edu
#Christian Wachinger, MIT, wachinge@mit.edu
#Bo Wang, Univ UT-SCI Institute, bowang@sci.utah.edu
#Demian Wassermann, BWH, demian@bwh.harvard.edu
#David Welch, Univ Iowa, david-welch@uiowa.edu
#William Wells, BWH/HMS, sw@bwh.harvard.edu
#Phillip White, BWH/HMS, white@bwh.harvard.edu
#Alex Yarmarkovich, Isomics Inc, alexy@bwh.harvard.edu
#Kitaro Yoshimitsu, BWH, kitarof1@bwh.harvard.edu
#Yang Yu, Rutgers Univ, yyu@cs.rutgers.edu
#Paolo Zaffino, Univ Magna Graecia of Catanzaro (Italy), p.zaffino@unicz.it
#Lilla Zollei, MGH, lzollei@nmr.mgh.harvard.edu

Analysis of different atlas-based segmentation techniques for parotid glands

2013-06-21T13:12:48Z

Kfritscher76: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image: NAMIC_HeadNeck_segmentation.png|Parotid gland + brainstem
</gallery>

==Key Investigators==
* MIT: Christian Wachinger, Matthew Brennan
* MGH: Karl Fritscher, Greg Sharp

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

<h3>Objective</h3>
Our goal is to investigate various segmentation approaches for identifying parotid glands on head and neck CT images. The focus will be on atlas-based methods, which exploit the information from a number of previously labeled images. Several different strategies exist on how to employ this prior information to achieve the segmentation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

We will try to work out the differences in terms of parameterization and regularization of various atlas-based methods. We will further try to characterize properties of such methods for the segmentation of parotid glands, which show high structural variability. Finally, we would like to investigate, which combination of methods may be promising.

</div>

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

<h3>Progress</h3>

3 different segmentation approaches based on the usage of multiple atlases [1], statistical appearance models [2] and a method based on using image patches in combination with Gaussian processes [3] have been tested for their suitability to segment the parotid gland.

'''Available methods:<br>'''

*LF = Image-based Registration with Label Fusion
*SAM = Statistical Appearance Models (label map registration, PCA, etc)
*PatchClass = Patch classification with Random Forests
*GP = Gaussian Processes for label refinement
<br>
'''20 test datasets (CTs) of Head-Neck region have been exchanged'''
<br><br>
'''The following hybrid approach has already been tested:'''

*LF -> SAM
<br>
'''First tests using following pipelines will be carried out:'''
*PatchClass -> SAM<br>
*LF -> GP
<br>
'''Further potential combinations:<br>'''
*PatchClass -> SAM -> GP
*LF -> GP -> SAM

</div>
</div>

==References==
* [1] Peroni M, Methods and Algorithms for Image Guided Adaptive Radio- and Hadron Therapy. PhD Thesis, Politecnico di Milano, 2011
* [2] Fritscher KD, Gruenerbl A, Schubert R, 3D image segmentation using combined shape-intensity prior models. Journal of Computer Assisted Radiology and Surgery, 2007;1:341–350
* [3] Wachinger C, Sharp G, Golland P, Contour-Driven Regression for Label Inference in Atlas-Based Segmentation, MICCAI, 2013.

Analysis of different atlas-based segmentation techniques for parotid glands

2013-06-21T13:11:31Z

Kfritscher76: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image: NAMIC_HeadNeck_segmentation.png|Parotid gland + brainstem
</gallery>

==Key Investigators==
* MIT: Christian Wachinger, Matthew Brennan
* MGH: Karl Fritscher, Greg Sharp

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

<h3>Objective</h3>
Our goal is to investigate various segmentation approaches for identifying parotid glands on head and neck CT images. The focus will be on atlas-based methods, which exploit the information from a number of previously labeled images. Several different strategies exist on how to employ this prior information to achieve the segmentation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

We will try to work out the differences in terms of parameterization and regularization of various atlas-based methods. We will further try to characterize properties of such methods for the segmentation of parotid glands, which show high structural variability. Finally, we would like to investigate, which combination of methods may be promising.

</div>

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

<h3>Progress</h3>

3 different segmentation approaches based on the usage of multiple atlases [1], statistical appearance models [2] and a method based on using image patches in combination with Gaussian processes for segmentation have been tested for their suitability to segment the parotid gland. Different approaches to combine the atlas and model based approaches [1,2] in different ways are currently under development.

'''Methods:<br>'''

*LF = Image-based Registration with Label Fusion
*SAM = Statistical Appearance Models (label map registration, PCA, etc)
*PatchClass = Patch classification with Random Forests
*GP = Gaussian Processes for label refinement
<br>
'''20 test datasets (CTs) of Head-Neck region have been exchanged'''
<br><br>
'''The following hybrid approach has already been tested:'''

*LF -> SAM
<br>
'''First tests using following pipelines will be carried out:'''
*PatchClass -> SAM<br>
*LF -> GP
<br>
'''Further potential combinations:<br>'''
*PatchClass -> SAM -> GP
*LF -> GP -> SAM

</div>
</div>

==References==
* [1] Peroni M, Methods and Algorithms for Image Guided Adaptive Radio- and Hadron Therapy. PhD Thesis, Politecnico di Milano, 2011
* [2] Fritscher KD, Gruenerbl A, Schubert R, 3D image segmentation using combined shape-intensity prior models. Journal of Computer Assisted Radiology and Surgery, 2007;1:341–350
* [3] Wachinger C, Sharp G, Golland P, Contour-Driven Regression for Label Inference in Atlas-Based Segmentation, MICCAI, 2013.

Analysis of different atlas-based segmentation techniques for parotid glands

2013-06-21T13:09:52Z

Kfritscher76: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image: NAMIC_HeadNeck_segmentation.png|Parotid gland + brainstem
</gallery>

==Key Investigators==
* MIT: Christian Wachinger, Matthew Brennan
* MGH: Karl Fritscher, Greg Sharp

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

<h3>Objective</h3>
Our goal is to investigate various segmentation approaches for identifying parotid glands on head and neck CT images. The focus will be on atlas-based methods, which exploit the information from a number of previously labeled images. Several different strategies exist on how to employ this prior information to achieve the segmentation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

We will try to work out the differences in terms of parameterization and regularization of various atlas-based methods. We will further try to characterize properties of such methods for the segmentation of parotid glands, which show high structural variability. Finally, we would like to investigate, which combination of methods may be promising.

</div>

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

<h3>Progress</h3>

3 different segmentation approaches based on the usage of multiple atlases [1], statistical appearance models [2] and a method based on using image patches in combination with Gaussian processes for segmentation have been tested for their suitability to segment the parotid gland using a dataset of 18 CT images. Different approaches to combine the atlas and model based approaches [1,2] in different ways are currently under development.

'''Methods:<br>'''

*LF = Image-based Registration with Label Fusion
*SAM = Statistical Appearance Models (label map registration, PCA, etc)
*PatchClass = Patch classification with Random Forest
*GP = Gaussian Processes for label refinement
<br>
'''20 test datasets (CTs) of Head-Neck region have been exchanged'''
<br><br>
'''The following hybrid approach has already been tested:'''

*LF -> SAM
<br>
'''First tests using following pipelines will be carried out:'''
*PatchClass -> SAM<br>
*LF -> GP
<br>
'''Further potential combinations:<br>'''
*PatchClass -> SAM -> GP
*LF -> GP -> SAM

</div>
</div>

==References==
* [1] Peroni M, Methods and Algorithms for Image Guided Adaptive Radio- and Hadron Therapy. PhD Thesis, Politecnico di Milano, 2011
* [2] Fritscher KD, Gruenerbl A, Schubert R, 3D image segmentation using combined shape-intensity prior models. Journal of Computer Assisted Radiology and Surgery, 2007;1:341–350
* [3] Wachinger C, Sharp G, Golland P, Contour-Driven Regression for Label Inference in Atlas-Based Segmentation, MICCAI, 2013.

Analysis of different atlas-based segmentation techniques for parotid glands

2013-06-21T13:05:19Z

Kfritscher76: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image: NAMIC_HeadNeck_segmentation.png|Parotid gland + brainstem
</gallery>

==Key Investigators==
* MIT: Christian Wachinger, Matthew Brennan
* MGH: Karl Fritscher, Greg Sharp

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

<h3>Objective</h3>
Our goal is to investigate various segmentation approaches for identifying parotid glands on head and neck CT images. The focus will be on atlas-based methods, which exploit the information from a number of previously labeled images. Several different strategies exist on how to employ this prior information to achieve the segmentation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

We will try to work out the differences in terms of parameterization and regularization of various atlas-based methods. We will further try to characterize properties of such methods for the segmentation of parotid glands, which show high structural variability. Finally, we would like to investigate, which combination of methods may be promising.

</div>

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

<h3>Progress</h3>

3 different segmentation approaches based on the usage of multiple atlases [1], statistical appearance models [2] and a method based on using image patches in combination with Gaussian processes for segmentation have been tested for their suitability to segment the parotid gland using a dataset of 18 CT images. Different approaches to combine the atlas and model based approaches [1,2] in different ways are currently under development.

'''Methods:<br>'''

*LF = Image-based Registration with Label Fusion
*SAM = Statistical Appearance Models (label map registration, PCA, etc)
*PatchClass = Patch classification with Random Forest
*GP = Gaussian Processes for label refinement

<br>20 test datasets (CTs) of Head-Neck region have been exchanged

'''First tests using following pipelines will be carried out:'''
*PatchClass -> SAM<br>
*LF -> GP<br>
'''
'''Further potential combinations:<br>'''
*PatchClass -> SAM -> GP
*LF -> GP -> SAM

</div>
</div>

==References==
* [1] Peroni M, Methods and Algorithms for Image Guided Adaptive Radio- and Hadron Therapy. PhD Thesis, Politecnico di Milano, 2011
* [2] Fritscher KD, Gruenerbl A, Schubert R, 3D image segmentation using combined shape-intensity prior models. Journal of Computer Assisted Radiology and Surgery, 2007;1:341–350
* [3] Wachinger C, Sharp G, Golland P, Contour-Driven Regression for Label Inference in Atlas-Based Segmentation, MICCAI, 2013.

Analysis of different atlas-based segmentation techniques for parotid glands

2013-06-21T13:01:06Z

Kfritscher76: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image: NAMIC_HeadNeck_segmentation.png|Parotid gland + brainstem
</gallery>

==Key Investigators==
* MIT: Christian Wachinger, Matthew Brennan
* MGH: Karl Fritscher, Greg Sharp

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

<h3>Objective</h3>
Our goal is to investigate various segmentation approaches for identifying parotid glands on head and neck CT images. The focus will be on atlas-based methods, which exploit the information from a number of previously labeled images. Several different strategies exist on how to employ this prior information to achieve the segmentation.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

We will try to work out the differences in terms of parameterization and regularization of various atlas-based methods. We will further try to characterize properties of such methods for the segmentation of parotid glands, which show high structural variability. Finally, we would like to investigate, which combination of methods may be promising.

</div>

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

<h3>Progress</h3>

3 different segmentation approaches based on the usage of multiple atlases [1], statistical appearance models [2] and a method based on using image patches in combination with Gaussian processes for segmentation have been tested for their suitability to segment the parotid gland using a dataset of 18 CT images. Different approaches to combine the atlas and model based approaches [1,2] in different ways are currently under development.

Methods:
LF = Image-based Registration with Label Fusion
SAM = Statistical Appearance Models (label map registration, PCA, etc)
PatchClass = Patch classification with Random Forest
GP = Gaussian Processes for label refinement

First tests using following pipelines:
PatchClass -> SAM
LF -> GP
Other combinations:
PatchClass -> SAM -> GP
LF -> GP -> SAM

</div>
</div>

==References==
* [1] Peroni M, Methods and Algorithms for Image Guided Adaptive Radio- and Hadron Therapy. PhD Thesis, Politecnico di Milano, 2011
* [2] Fritscher KD, Gruenerbl A, Schubert R, 3D image segmentation using combined shape-intensity prior models. Journal of Computer Assisted Radiology and Surgery, 2007;1:341–350
* [3] Wachinger C, Sharp G, Golland P, Contour-Driven Regression for Label Inference in Atlas-Based Segmentation, MICCAI, 2013.

2013 Summer Project Week: Computer Assisted Surgery

2013-06-21T12:58:04Z

Kfritscher76:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:VirtualFractureReconstruction_ExtScreen.png|Virtual Fracture Reconstruction Extension
</gallery>
<br>
[[Image:VirtualFractureReconstruction_Screen.png|600px|thumb|center|Screenshot of Virtual Fracture Reconstruction Module]]
==Key Investigators==
* MGH/BWH/UMIT: Karl Fritscher
* GATECH: Peter Karasev
* BWH: Ron Kikinis, Steve Pieper

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

<h3>Objective</h3>
The repositioning of fracture fragments during a surgical intervention often requires a trial and error approach, which leads to a significant prolongation of the surgery and causes additional trauma to the fragments and the surrounding soft tissue. Wound healing failure, infections, or joint stiffness can be the consequence. Therefore, there is a clear trend towards the development of less invasive techniques to reconstruct complex fractures. In order to support this trend, software tools for calculating and visualizing the optimal way of repositioning fracture fragments based on the usage of segmented CT images as input data have been developed. In several studies, they have successfully demonstrated their potential to decrease operation times and increase reduction accuracy. However, existing software tools are often restricted to particular types of fractures and require a large amount of user interaction.
Hence, the main objective of the project is to overcome these limitations by developing an algorithmic pipeline that is calculating and visualizing the optimal way of repositioning fracture fragments with a minimal amount of user interaction and without restrictions to particular types of fractures.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

Novel concepts like the usage of prior knowledge about the shape of the healthy (=non-fractured) bone during the reconstruction process will be applied. Moreover, information coming from CT volumes will be combined with information on geometric surface properties in order to identify corresponding (fragment) surface points.

</div>

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

<h3>Progress</h3>

*A slicer command line module + loadable module has been created.
*An extension has been for the modules.
*The two modules have been uploaded as an extension.
*CDash output: 0 warnings, 0 errors (testen on Linux and Windows 64bit)
*WIKI page for extension available here: http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Extensions/VirtualFractureReconstruction
</div>
</div>

2013 Summer Project Week: Computer Assisted Surgery

2013-06-21T12:57:36Z

Kfritscher76:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:VirtualFractureReconstruction_ExtScreen.png|Virtual Fracture Reconstruction Extension
</gallery>
<br>
[[Image:VirtualFractureReconstruction_Screen.png|600px|thumb|left|Screenshot of Virtual Fracture Reconstruction Module]]
==Key Investigators==
* MGH/BWH/UMIT: Karl Fritscher
* GATECH: Peter Karasev
* BWH: Ron Kikinis, Steve Pieper

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

<h3>Objective</h3>
The repositioning of fracture fragments during a surgical intervention often requires a trial and error approach, which leads to a significant prolongation of the surgery and causes additional trauma to the fragments and the surrounding soft tissue. Wound healing failure, infections, or joint stiffness can be the consequence. Therefore, there is a clear trend towards the development of less invasive techniques to reconstruct complex fractures. In order to support this trend, software tools for calculating and visualizing the optimal way of repositioning fracture fragments based on the usage of segmented CT images as input data have been developed. In several studies, they have successfully demonstrated their potential to decrease operation times and increase reduction accuracy. However, existing software tools are often restricted to particular types of fractures and require a large amount of user interaction.
Hence, the main objective of the project is to overcome these limitations by developing an algorithmic pipeline that is calculating and visualizing the optimal way of repositioning fracture fragments with a minimal amount of user interaction and without restrictions to particular types of fractures.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

Novel concepts like the usage of prior knowledge about the shape of the healthy (=non-fractured) bone during the reconstruction process will be applied. Moreover, information coming from CT volumes will be combined with information on geometric surface properties in order to identify corresponding (fragment) surface points.

</div>

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

<h3>Progress</h3>

*A slicer command line module + loadable module has been created.
*An extension has been for the modules.
*The two modules have been uploaded as an extension.
*CDash output: 0 warnings, 0 errors (testen on Linux and Windows 64bit)
*WIKI page for extension available here: http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Extensions/VirtualFractureReconstruction
</div>
</div>

2013 Summer Project Week: Computer Assisted Surgery

2013-06-21T12:54:02Z

Kfritscher76:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:VirtualFractureReconstruction_ExtScreen.png|Virtual Fracture Reconstruction Extension

</gallery>

[[Image:VirtualFractureReconstruction_Screen.png|600px|thumb|center|Screenshot of Virtual Fracture Reconstruction Module]]
==Key Investigators==
* MGH/BWH/UMIT: Karl Fritscher
* GATECH: Peter Karasev
* BWH: Ron Kikinis, Steve Pieper

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

<h3>Objective</h3>
The repositioning of fracture fragments during a surgical intervention often requires a trial and error approach, which leads to a significant prolongation of the surgery and causes additional trauma to the fragments and the surrounding soft tissue. Wound healing failure, infections, or joint stiffness can be the consequence. Therefore, there is a clear trend towards the development of less invasive techniques to reconstruct complex fractures. In order to support this trend, software tools for calculating and visualizing the optimal way of repositioning fracture fragments based on the usage of segmented CT images as input data have been developed. In several studies, they have successfully demonstrated their potential to decrease operation times and increase reduction accuracy. However, existing software tools are often restricted to particular types of fractures and require a large amount of user interaction.
Hence, the main objective of the project is to overcome these limitations by developing an algorithmic pipeline that is calculating and visualizing the optimal way of repositioning fracture fragments with a minimal amount of user interaction and without restrictions to particular types of fractures.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

Novel concepts like the usage of prior knowledge about the shape of the healthy (=non-fractured) bone during the reconstruction process will be applied. Moreover, information coming from CT volumes will be combined with information on geometric surface properties in order to identify corresponding (fragment) surface points.

</div>

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

<h3>Progress</h3>

*A slicer command line module + loadable module has been created.
*An extension has been for the modules.
*The two modules have been uploaded as an extension.
*CDash output: 0 warnings, 0 errors (testen on Linux and Windows 64bit)
*WIKI page for extension available here: http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Extensions/VirtualFractureReconstruction
</div>
</div>

2013 Summer Project Week: Computer Assisted Surgery

2013-06-21T12:49:45Z

Kfritscher76:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:VirtualFractureReconstruction_ExtScreen.png|Virtual Fracture Reconstruction Extension
Image:VirtualFractureReconstruction_Screen.png|Screenshot of Virtual Fracture Reconstruction Module
</gallery>

==Key Investigators==
* MGH/BWH/UMIT: Karl Fritscher
* GATECH: Peter Karasev
* BWH: Ron Kikinis, Steve Pieper

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

<h3>Objective</h3>
The repositioning of fracture fragments during a surgical intervention often requires a trial and error approach, which leads to a significant prolongation of the surgery and causes additional trauma to the fragments and the surrounding soft tissue. Wound healing failure, infections, or joint stiffness can be the consequence. Therefore, there is a clear trend towards the development of less invasive techniques to reconstruct complex fractures. In order to support this trend, software tools for calculating and visualizing the optimal way of repositioning fracture fragments based on the usage of segmented CT images as input data have been developed. In several studies, they have successfully demonstrated their potential to decrease operation times and increase reduction accuracy. However, existing software tools are often restricted to particular types of fractures and require a large amount of user interaction.
Hence, the main objective of the project is to overcome these limitations by developing an algorithmic pipeline that is calculating and visualizing the optimal way of repositioning fracture fragments with a minimal amount of user interaction and without restrictions to particular types of fractures.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

Novel concepts like the usage of prior knowledge about the shape of the healthy (=non-fractured) bone during the reconstruction process will be applied. Moreover, information coming from CT volumes will be combined with information on geometric surface properties in order to identify corresponding (fragment) surface points.

</div>

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

<h3>Progress</h3>

*A slicer command line module + loadable module has been created.
*An extension has been for the modules.
*The two modules have been uploaded as an extension.
*CDash output: 0 warnings, 0 errors (testen on Linux and Windows 64bit)
*WIKI page for extension available here: http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Extensions/VirtualFractureReconstruction
</div>
</div>

2013 Summer Project Week: Computer Assisted Surgery

2013-06-21T12:47:50Z

Kfritscher76:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:VirtualFractureReconstruction_ExtScreen.png|Virtual Fracture Reconstruction Extension
Image:VirtualFractureReconstruction_Screen.png|Screenshot of Virtual Fracture Reconstruction Module
</gallery>

==Key Investigators==
* MGH/BWH/UMIT: Karl Fritscher
* GATECH: Peter Karasev
* BWH: Ron Kikinis, Steve Pieper

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

<h3>Objective</h3>
The repositioning of fracture fragments during a surgical intervention often requires a trial and error approach, which leads to a significant prolongation of the surgery and causes additional trauma to the fragments and the surrounding soft tissue. Wound healing failure, infections, or joint stiffness can be the consequence. Therefore, there is a clear trend towards the development of less invasive techniques to reconstruct complex fractures. In order to support this trend, software tools for calculating and visualizing the optimal way of repositioning fracture fragments based on the usage of segmented CT images as input data have been developed. In several studies, they have successfully demonstrated their potential to decrease operation times and increase reduction accuracy. However, existing software tools are often restricted to particular types of fractures and require a large amount of user interaction.
Hence, the main objective of the project is to overcome these limitations by developing an algorithmic pipeline that is calculating and visualizing the optimal way of repositioning fracture fragments with a minimal amount of user interaction and without restrictions to particular types of fractures.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

Novel concepts like the usage of prior knowledge about the shape of the healthy (=non-fractured) bone during the reconstruction process will be applied. Moreover, information coming from CT volumes will be combined with information on geometric surface properties in order to identify corresponding (fragment) surface points.

</div>

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

<h3>Progress</h3>

*A slicer command line module + loadable module has been created.
*An extension has been for the modules.
*The two modules have been uploaded as an extension.
*CDash output: 0 warnings, 0 errors (testen on Linux and Windows 64bit)
*WIKI page for extension available here: http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Extensions/VirtualFractureReconstruction
</div>
</div>

==Delivery Mechanism==

The code for automated fracture reconstruction will be provided as Slicer extension.

File:VirtualFractureReconstruction Screen.png

2013-06-21T12:43:50Z

Kfritscher76:

File:VirtualFractureReconstruction ExtScreen2.png

2013-06-21T12:39:35Z

Kfritscher76:

File:VirtualFractureReconstruction ExtScreen.png

2013-06-21T12:39:24Z

Kfritscher76:

2013 Summer Project Week: Computer Assisted Surgery

2013-06-20T22:23:49Z

Kfritscher76:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:Fracture1.png
</gallery>

==Key Investigators==
* MGH/BWH/UMIT: Karl Fritscher
* GATECH: Peter Karasev
* BWH: Ron Kikinis, Steve Pieper

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

<h3>Objective</h3>
The repositioning of fracture fragments during a surgical intervention often requires a trial and error approach, which leads to a significant prolongation of the surgery and causes additional trauma to the fragments and the surrounding soft tissue. Wound healing failure, infections, or joint stiffness can be the consequence. Therefore, there is a clear trend towards the development of less invasive techniques to reconstruct complex fractures. In order to support this trend, software tools for calculating and visualizing the optimal way of repositioning fracture fragments based on the usage of segmented CT images as input data have been developed. In several studies, they have successfully demonstrated their potential to decrease operation times and increase reduction accuracy. However, existing software tools are often restricted to particular types of fractures and require a large amount of user interaction.
Hence, the main objective of the project is to overcome these limitations by developing an algorithmic pipeline that is calculating and visualizing the optimal way of repositioning fracture fragments with a minimal amount of user interaction and without restrictions to particular types of fractures.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

Novel concepts like the usage of prior knowledge about the shape of the healthy (=non-fractured) bone during the reconstruction process will be applied. Moreover, information coming from CT volumes will be combined with information on geometric surface properties in order to identify corresponding (fragment) surface points.

</div>

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

<h3>Progress</h3>

A slicer command line module + loadable module has been created.
An extension has been for the modules.
The two modules have been uploaded as an extension.
CDash output: 0 warnings, 0 errors

</div>
</div>

==Delivery Mechanism==

The code for automated fracture reconstruction will be provided as Slicer extension.

2013 Summer Project Week: Computer Assisted Surgery

2013-06-17T18:18:10Z

Kfritscher76:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:Fracture1.png
</gallery>

==Key Investigators==
* MGH/BWH/UMIT: Karl Fritscher
* GATECH: Peter Karasev
* BWH: Ron Kikinis, Steve Pieper

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

<h3>Objective</h3>
The repositioning of fracture fragments during a surgical intervention often requires a trial and error approach, which leads to a significant prolongation of the surgery and causes additional trauma to the fragments and the surrounding soft tissue. Wound healing failure, infections, or joint stiffness can be the consequence. Therefore, there is a clear trend towards the development of less invasive techniques to reconstruct complex fractures. In order to support this trend, software tools for calculating and visualizing the optimal way of repositioning fracture fragments based on the usage of segmented CT images as input data have been developed. In several studies, they have successfully demonstrated their potential to decrease operation times and increase reduction accuracy. However, existing software tools are often restricted to particular types of fractures and require a large amount of user interaction.
Hence, the main objective of the project is to overcome these limitations by developing an algorithmic pipeline that is calculating and visualizing the optimal way of repositioning fracture fragments with a minimal amount of user interaction and without restrictions to particular types of fractures.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

Novel concepts like the usage of prior knowledge about the shape of the healthy (=non-fractured) bone during the reconstruction process will be applied. Moreover, information coming from CT volumes will be combined with information on geometric surface properties in order to identify corresponding (fragment) surface points.

</div>

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

<h3>Progress/Goals</h3>

A slicer command line module + loadable module has been created. Until the project week they will be available as an extension. Different implmentation details like usage of CUDA code and improved manual interactions shall be discussed in the project week.

</div>
</div>

==Delivery Mechanism==

The code for automated fracture reconstruction will be provided as Slicer extension.

2013 Summer Project Week: Computer Assisted Surgery

2013-06-17T17:51:38Z

Kfritscher76:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:Fracture1.png
</gallery>

==Key Investigators==
* MGH/BWH/UMIT: Karl Fritscher
* GATECH: Peter Karasev
* BWH: Ron Kikinis

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

<h3>Objective</h3>
The repositioning of fracture fragments during a surgical intervention often requires a trial and error approach, which leads to a significant prolongation of the surgery and causes additional trauma to the fragments and the surrounding soft tissue. Wound healing failure, infections, or joint stiffness can be the consequence. Therefore, there is a clear trend towards the development of less invasive techniques to reconstruct complex fractures. In order to support this trend, software tools for calculating and visualizing the optimal way of repositioning fracture fragments based on the usage of segmented CT images as input data have been developed. In several studies, they have successfully demonstrated their potential to decrease operation times and increase reduction accuracy. However, existing software tools are often restricted to particular types of fractures and require a large amount of user interaction.
Hence, the main objective of the project is to overcome these limitations by developing an algorithmic pipeline that is calculating and visualizing the optimal way of repositioning fracture fragments with a minimal amount of user interaction and without restrictions to particular types of fractures.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

Novel concepts like the usage of prior knowledge about the shape of the healthy (=non-fractured) bone during the reconstruction process will be applied. Moreover, information coming from CT volumes will be combined with information on geometric surface properties in order to identify corresponding (fragment) surface points.

</div>

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

<h3>Progress/Goals</h3>

A slicer command line module + loadable module has been created. Until the project week they will be available as an extension. Different implmentation details like usage of CUDA code and improved manual interactions shall be discussed in the project week.

</div>
</div>

==Delivery Mechanism==

The code for automated fracture reconstruction will be provided as Slicer extension.

2013 Summer Project Week: Computer Assisted Surgery

2013-06-17T17:50:58Z

Kfritscher76:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:Fracture1.png|[[2013_Summer_Project_Week#Projects|Complex fracture]]
</gallery>

==Key Investigators==
* MGH/BWH/UMIT: Karl Fritscher
* GATECH: Peter Karasev
* BWH: Ron Kikinis

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

<h3>Objective</h3>
The repositioning of fracture fragments during a surgical intervention often requires a trial and error approach, which leads to a significant prolongation of the surgery and causes additional trauma to the fragments and the surrounding soft tissue. Wound healing failure, infections, or joint stiffness can be the consequence. Therefore, there is a clear trend towards the development of less invasive techniques to reconstruct complex fractures. In order to support this trend, software tools for calculating and visualizing the optimal way of repositioning fracture fragments based on the usage of segmented CT images as input data have been developed. In several studies, they have successfully demonstrated their potential to decrease operation times and increase reduction accuracy. However, existing software tools are often restricted to particular types of fractures and require a large amount of user interaction.
Hence, the main objective of the project is to overcome these limitations by developing an algorithmic pipeline that is calculating and visualizing the optimal way of repositioning fracture fragments with a minimal amount of user interaction and without restrictions to particular types of fractures.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Plan</h3>

Novel concepts like the usage of prior knowledge about the shape of the healthy (=non-fractured) bone during the reconstruction process will be applied. Moreover, information coming from CT volumes will be combined with information on geometric surface properties in order to identify corresponding (fragment) surface points.

</div>

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

<h3>Progress/Goals</h3>

A slicer command line module + loadable module has been created. Until the project week they will be available as an extension. Different implmentation details like usage of CUDA code and improved manual interactions shall be discussed in the project week.

</div>
</div>

==Delivery Mechanism==

The code for automated fracture reconstruction will be provided as Slicer extension.

File:Fracture1.png

2013-06-17T17:48:21Z

Kfritscher76: