NAMIC Wiki - User contributions [en]

2017 Winter Project Week/LORDWI

2017-01-06T14:51:13Z

Westin: /* Key Investigators */

==Key Investigators==
* Henrik Groenholt Jensen, UCPH
* Lauren J. O'Donnell, BWH
* Tina Kapur, BWH
* Fan Zhang, BWH
* Carl-Fredrik Westin, BWH

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Evaluate/validate the density-based registration framework for DWI developed at UCPH (this is a nonrigid model - see the link below for a paper on the global algorithm).
|

* Figure out what diffusion data is available (preferably data already evaluated by another registration model).
* Discuss best ways to validate results (tractography, biomarkers, synthetic data, phantoms, others?). So far we have visually tested inter-subject registrations of HCP data and intra-subject multi-shell, and intra-subject on young brain tumor subjects.
* Consider if Slicer can be used in tandem for evaluation.
* This method introduces Mutual Information to nonrigid registration of DWI. Discuss if other similarity measures (e.g. correlation measures) would be better suited for specific problems.
|

*
|}

==Background and References==

* MICCAI paper on early framework: http://link.springer.com/chapter/10.1007/978-3-319-24571-3_37

2017 Winter Project Week

2017-01-06T14:50:39Z

Westin: /* dMRI */

__NOTOC__
[[image:PW-Winter2017.png|300px]]

=Welcome to the web page for the 24th Project Week!=

The 24th NA-MIC Project Week open source hackathon is being held during the week of January 9-13, 2017 at MIT. Please go through this page for information, and if you have questions, please contact [https://www.spl.harvard.edu/pages/People/tkapur Tina Kapur, PhD].

==Logistics==

*'''Dates:''' January 9-13, 2017.
*'''Location:''' [https://www.google.com/maps/place/MIT:+Computer+Science+and+Artificial+Intelligence+Laboratory/@42.361864,-71.090563,16z/data=!4m2!3m1!1s0x0:0x303ada1e9664dfed?hl=en MIT CSAIL], Cambridge, MA. (Rooms: [[MIT_Project_Week_Rooms#Kiva|Kiva]], R&D)
*'''Transportation:''' Public transportation is highly encouraged, as no parking permits will be issued by MIT. For a list of local garages, please see [[http://web.mit.edu/facilities/transportation/parking/visitors/public_parking.html here]]
*'''REGISTRATION:''' Register [https://www.regonline.com/2017projectweek here]. Registration Fee: $330.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Next Project Week:'''' [http://wiki.na-mic.org/Wiki/index.php/2017_Summer_Project_Week June 26-30, 2017, Catanzaro, Italy]

== Introduction ==
The National Alliance for Medical Image Computing (NAMIC), was founded in 2005 and chartered with building a computational infrastructure to support biomedical research as part of the NIH funded [http://www.ncbcs.org/ NCBC] program. The work of this alliance has resulted in important progress in algorithmic research, an open source medical image computing platform [http://www.slicer.org 3D Slicer], enhancements to the underlying building blocks [http://www.vtk.org VTK], [http://www.itk.org ITK], [http://www.cmake.org CMake], and [http://www.cdash.org CDash], and the creation of a community of algorithm researchers, biomedical scientists and software engineers who are committed to open science. This community meets twice a year in an open source hackathon event called Project Week.

[[Engineering:Programming_Events|Project Week]] is a semi-annual open source hackathon which draws 60-120 researchers. As of August 2014, it is a [http://www.miccai.org/organization MICCAI] endorsed event. The participants work collaboratively on open-science solutions for problems that lie on the interfaces of the fields of computer science, mechanical engineering, biomedical engineering, and medicine. In contrast to conventional conferences and workshops the primary focus of the Project Weeks is to make progress in projects (as opposed to reporting about progress). The objective of the Project Weeks is to provide a venue for this community of medical open source software creators. Project Weeks are open to all, are publicly advertised, and are funded through fees paid by the attendees. Participants are encouraged to stay for the entire event.

Project Week activities: Everyone shows up with a project. Some people are working on the platform. Some people are developing algorithms. Some people are applying the tools to their research problems. We begin the week by introducing projects and connecting teams. We end the week by reporting progress. In addition to the ongoing working sessions, breakout sessions are organized ad-hoc on a variety of special topics. These topics include: discussions of software architecture, presentations of new features and approaches and topics such as Image-Guided Therapy.

Several funded projects use the Project Week as a place to convene and collaborate. These include [http://nac.spl.harvard.edu/ NAC], [http://www.ncigt.org/ NCIGT], [http://qiicr.org/ QIICR], and [http://ocairo.technainstitute.com/open-source-software-platforms-and-databases-for-the-adaptive-process/ OCAIRO].

A summary of all previous Project Events is available [[Project_Events#Past_Project_Weeks|here]].

Please make sure that you are on the NA-MIC Project Week [http://public.kitware.com/mailman/listinfo/na-mic-project-week mailing list]

==Conference Calls for Preparation==

Conference call phone number and notes are available [[TCONS:2017_Winter_Project_Week|here]].

==Calendar==

'''''<font color="maroon">The events are listed in the calendar below. Note that due to a current known limitation of our infrastructure, you will need to manually navigate to the week of January 8, 2017 to see the relevant events.</font>'''''<br>

{{#widget:Google Calendar
|id=kitware.com_sb07i171olac9aavh46ir495c4@group.calendar.google.com
|timezone=America/New_York&dates=20170108%2F20170114
|title=NAMIC Winter Project Week
|view=WEEK
|dates=20170108/20170114
}}

iCal (.ics) link: https://calendar.google.com/calendar/ical/kitware.com_sb07i171olac9aavh46ir495c4%40group.calendar.google.com/public/basic.ics

='''Projects'''=

*Use this [[2017_Project_Week_Template | Updated Template for project pages]]

== Learning and GPUs ==

* [[2017 Winter Project Week/Needle Segmentation from MRI | Needle Segmentation from MRI]] (Ziyang Wang, Guillaume Pernelle, Paolo Zaffino, Tina Kapur)
* [[2017 Winter Project Week/OCM-MRI | Deep Learning for Synthetic MRI]] (Frank Preiswerk, Yaofei "Ada" Wang)
* [[2017 Winter Project Week/An open-source tool to classify TMJ OA condyles | An open-source tool to classify TMJ OA condyles]] (Priscille de Dumast, Juan Carlos Prieto, Beatriz Paniagua)
* [[2017 Winter Project Week/DeepInfer| DeepInfer: Open-source Deep Learning Deployment Toolkit]] (Alireza Mehrtash, Mehran Pesteie, Yang (Silvia) Yixin, Tina Kapur, Sandy Wells, Purang Abolmaesumi, Andriy Fedorov)
* [[2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification | Evaluate Deep Learning for binary cancer lesion classification]] (Curt Lisle)

== Web Technologies ==
* [[2017_Winter_Project_Week/WebTechnologyAndSlicer| Web Technology and Slicer]] (Steve Pieper, Erik Zeigler, Curt Lisle, Satra Ghosh, Hans Meine)
* [[2017 Winter Project Week/Slicer Qt5 and Python3 | Slicer Qt5 and Python3]] (Steve Pieper, Jean-Christophe Fillion-Robin, Andras Lasso, Andrey Fedorov)
* [[2017_Winter_Project_Week/OAuth2SlicerPathology | OAuth2.0 authentication in SlicerPathology]] (Erich Bremer, Steve Pieper)
* [[Explore integration of Web-based imaging workflows with Slicer | Explore integration of Web-based imaging workflows with Slicer ]] (Curt Lisle, Satra Gosh, Steve Peiper)
* [[2017 Winter Project Week/IPFS_NoSQL_Combination | IPFS and NoSQL for cloud databases]] (Hans Meine, Steve Pieper)
* [[2017 Winter Project Week/Web-based system to federate biological, clinical and morphological data | Web-based system to federate biological, clinical and morphological data]] (Juan Carlos Prieto, Clément Mirabel)
*[[2017 Winter Project Week/Electron App to add, navigate and visualize DICOM images | Electron App to add, navigate and visualize DICOM images
]] (Smruti Padhy, Satrajit Ghosh, Mathias Goncalves)

== IGT: Navigation, Robotics, Surgical Planning ==
* [[2017 Winter Project Week/Tracked Ultrasound Standardization | Tracked Ultrasound Standardization III: The Refining]] (Andras Lasso, Simon Drouin, Junichi Tokuda, Longquan Chen, Adam Rankin, Janne Beate Bakeng)
* [[2017 Winter Project Week/ROS Surface Scan | ROS Surface Scan]] (Tobias Frank, Junichi Tokuda, Longquan Chen)
* [[2017 Winter Project Week/Open_Source_Electromagnetic_Trackers | Open Source Electromagnetic Trackers]] (Peter Traneus Anderson)
* [[2017 Winter Project Week/OpenIGTLink for the Communications of Robotics Devices: Adding Kuka LWR connection to MeVisLab | OpenIGTLink for the Communications of Robotics Devices: Adding Kuka LWR connection to MeVisLab ]] (Scheherazade Kraß (Shery), Junichi Tokuda, Longquan Chen, )
* [[2017 Winter Project Week/LiverResectionPlanning | Liver resection planning extension]] (Louise Oram, Andrey Fedorov, Christian Herz, Andras Lasso)
* [[2017 Winter Project Week/ProstateSectorSegmentation | Prostate Gland Sector Segmentation]] (Anneke Meyer, Andrey Fedorov)
* [[2017 Winter Project Week/Multi-ModalitySegmentationOfUSandMRImagesForGliomaSurgery | Multi-Modality Segmentation of US- and MR-Images for Glioma Surgery]] (Jennifer Nitsch)
* [[2017 Winter Project Week/MeningiomaSegmentation | Segmentation of meningiomas in structural MR images]] (Satrajit Ghosh, Omar Arnaout)
* [[2017 Winter Project Week/CoronarySegmentationTool| Automatic and Manual Segmentation Tool of Coronary Artery from CTA imaging]] (Haoyin Zhou, Jayender Jagadeesan)

==dMRI==
* [[2017 Winter Project Week/WhiteMatterAnalysis | WhiteMatterAnalysis New Module and Documentation]] (Fan Zhang, Shun Gong, Isaiah Norton, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/LORDWI | Density-based DMRI registration ]] (Henrik Groenholt Jensen, Lauren J. O'Donnell, Tina Kapur, Fan Zhang, Carl-Fredrik Westin)
* [[2017 Winter Project Week/SlicerDMRIDocumentationAndTesting | SlicerDMRI Testing and Documentation]] (Isaiah Norton, Fan Zhang, Shun Gong, Ye Wu, Lauren J. O'Donnell)
* [[2017 Winter Project Week/DiPy_in_Slicer | DiPy integration in Slicer]] (Isaiah Norton, Lauren J. O'Donnell)
* [[2017 Winter Project Week/DWI_Similarity_Metrics | Identification of information-rich patches in Diffusion-Weighted Images]] (Laurent Chauvin, Fan Zhang, Lauren J. O'Donnell, Matthew Toews)

==Quantitative Imaging Informatics==
* [[2017 Winter Project Week/dcmqi | dcmqi library and DICOM QuantitativeReporting]] (Andrey Fedorov, Christian Herz, JC, Steve Pieper)
* [[2017 Winter Project Week/PyRadiomics | PyRadiomics library ]] (Joost van Griethuysen, Hugo Aerts, Andrey Fedorov, Steve Pieper, Jean-Christope Fillion-Robin)

== Visualization ==
* [[2017 Winter Project Week/Slicer_HoloLens | Slicer & HoloLens]] (Adam Rankin, Andras Lasso)
== Infrastructure ==
* [[2017 Winter Project Week/SubjectHierarchyRefactoring | Subject hierarchy single-node refactoring]] (Csaba Pinter)
* [[2017 Winter Project Week/SlicerGeometryModifier | Slicer support for interactive modification of 3D models ]] (Johan Andruejol, Beatriz Paniagua, Andras Lasso)
* [[2017 Winter Project Week/PkModeling | PkModeling - DCE Modeling Accuracy and UI/UX Update]] (Andrew Beers)
* [[2017 Winter Project Week/SegWithSubtractionAndModel| Manual Segmentation Module w/ Subtraction Maps + Delaunay Models]] (Andrew Beers)
* [[2017 Winter Project Week/UpdatingCommunityForums | Updating Community Forums (Discourse, GitHub, Gitter, ???)]] (Andrey Fedorov, Andras Lasso, Steve Pieper, Mike Halle, Isaiah Norton, and The Community)

==To be Categorized==

* [[2017 Winter Project Week/Support_for_volumetric_meshes | Support for volumetric meshes ]] (Alexis Girault, Curtis Lisle, Steve Piper)
* [[2017 Winter Project Week/Improve_Matlab_integration | Improve Matlab integration ]] (Alexis Girault, Andras Lasso)
* [[2017 Winter Project Week/HyperspectralOpht | Slicer for Hyperspectral Ophthalmology Analysis ]] (Sungmin Hong)
* [[2017 Winter Project Week/SlicerShape | Slicer for Shape Analysis ]] (Beatriz Paniagua)
* [[2017 Winter Project Week/MandibularRegression | Mandibular Shape Regression ]] (Beatriz Paniagua, James Fishbaugh)
* [[2017 Winter Project Week/Plastimatch19 | Upgrade Plastimatch extension ]] (Greg Sharp)
* [[2017 Winter Project Week/2017TutorialContest| Tutorial contest]] (Sonia Pujol)
* [[2017 Winter Project Week/GeodesicSegmentationandLungtumorAnalysis| Geodesic Segmentation and Lung tumor Analysis]] (Patmaa S, Sarthak Pati, Ratheesh k, Mark B, Yong F, Despina K, Ragini V, Christos D)

= '''Registrants''' =

Do not add your name to this list - it is maintained by the organizers based on your paid registration. To register, visit this [https://www.regonline.com/2017projectweek registration site].

# A, Zeina :: SHBOUL
# Aerts, Hugo :: DFCI-Harvard
# Alam, Mahbubul :: Old Dominion University
# Anderson, Peter :: Retired
# Andruejol, Johan :: Kitware, Inc.
# Bakeng, Janne Beate :: SINTEF
# Beers, Andrew :: Massachusetts General Hospital
# Bernal Rusiel, Jorge Luis :: Boston Children's Hospital
# Bremer, Erich :: Stony Brook University
# Burke, Brice :: American University of Antigua College of Medicine
# Cetin Karayumak, Suheyla :: Brigham and Women's Hospital
# Chae, Michael :: Monash University
# Chauvin, Laurent :: ETS
# Dalca, Adrian :: Massachusetts Institute of Technology
# Fedorov, Andriy :: Brigham and Women's Hospital
# Fillion-Robin, Jean-Christophe :: Kitware, Inc.
# Fishbaugh, James :: New York University
# Frank, Tobias :: Leibniz Universität Hannover
# García Mato, David :: Queen´s University / Universidad Carlos III de Madrid
# Girault, Alexis :: Kitware, Inc.
# Golland, Polina :: Massachusetts Institute of Technology
# Gollub, Randy :: Massachusetts General Hospital
# Gong, Shun :: Brigham and Women's Hospital
# Guerrier de Dumast, Priscille :: University of Michigan
# Harris, Gordon :: Massachusetts General Hospital
# Herz, Christian :: Brigham and Women's Hospital
# Hong, Sungmin :: New York University
# Hosny, Ahmed :: Dana-Farber
# Jagadeesan, Jayender :: Brigham and Women's Hospital
# Jensen, Henrik G. :: University of Copenhagen
# Kapur, Tina :: Brigham and Women's Hospital
# Kikinis, Ron :: Brigham and Women's Hospital
# Lasso, Andras :: PerkLab, Queen's University
# Lauer, Rebekka :: Humboldt University Berlin
# Lisle, Curtis :: KnowledgeVis, LLC
# Mastrogiacomo, Katie :: Brigham and Women's Hospital
# Mateus, D. :: TUM
# Mehrtash, Alireza :: Brigham and Women's Hospital
# Meine, Hans :: University of Bremen
# Meyer, Anneke :: University of Magdeburg
# Miller, James :: GE Research
# Mirabel, Clement :: University of Michigan
# Nitsch, Jennifer :: University of Bremen
# Norton, Isaiah :: Brigham and Women's Hospital
# O'Donnell, Lauren :: Brigham and Women's Hospital
# Oram, Louise :: The Intervention Centre-Oslo University Hospital
# Paniagua, Beatriz :: Kitware, Inc.
# Parmar, Chintan :: DFCI-Harvard Medical School
# Peled, Sharon :: Brigham and Women's Hospital
# Pieper, Steve :: Isomics, Inc.
# Pinter, Csaba :: Queen's University
# Preiswerk, Frank :: Brigham and Women's Hospital/Harvard Medical School
# Pujol, Sonia :: Brigham and Women's Hospital/Harvard Medical School
# Rankin, Adam :: Robarts Research Institute
# Rheault, Francois :: Université de Sherbrooke
# Roethe, Anna :: Humboldt University / Charité University Hospital Berlin
# Sharp, Gregory :: Massachusetts General Hospital
# Sridharan, Patmaa :: University of Pennsylvania-CBICA
# Vidyaratne, Lasitha :: Old Dominion University
# Wang, Yaofei :: Brigham and Women's Hospital
# Westin, Carl-Fredrik :: Brigham and Women's Hospital, Harvard Medical School
# Yang, Yixin :: Brigham and Women's Hospital
# Ye, Wu :: Brigham and Women's Hospital
# Zaffino, Paolo :: Magna Graecia University of Catanzaro, Italy
# Zeleznik, Roman :: DFCI
# Zhang, Fan :: Brigham and Women's Hospital
# Zhang, Miaomiao :: Massachusetts Institute of Technology
# Zhang, Yuqian :: Brigham and Women's Hospital
# Ziegler, Erik :: Open Health Imaging Foundation/Mass General Hospital

UKF Tractography in Slicer 4

2013-01-02T18:18:54Z

Westin: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW-SLC2013.png|[[2013_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==

* Ryan Eckbo, BWH, HMS
* Yogesh Rathi, BWH, HMS
* Demian Wassermann, BWH, HMS
* Carl-Fredrik Westin, BWH, HMS

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>

* To convert Dr. Rathi's UKF tractography Slicer3 module to a Slicer4 extension

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

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

Call for Identification of Medical Image Computing Grant Applications

2012-09-04T08:45:31Z

Westin: /* Signed */

[http://en.wikipedia.org/wiki/Medical_image_computing Medical Image Computing] (MIC) is a young field of research. To the best of our knowledge, there is no study section at NIH specializing on this topic. This is in contrast to the well established field of [http://en.wikipedia.org/wiki/Medical_imaging Medical Imaging], which is focused on improving image acquisition and reconstruction and has its own specialized study section.

We would like to raise the visibility of the field of Medical Image Computing (MIC) with the long term goal of initiating the creation of a study section focused on our field. This would provide a better match not only of the individual reviewers but also of the study section as a whole, which would be better attuned to MIC content. NIH needs evidence that enough grants on the topic are submitted at sufficient frequency to initiate this process. The typical threshold is around 20 submissions per cycle. Typically, an ad-hoc study section is created first, which can later be converted to a permanent study section if the stream of applications is sustained over several application cycles.

This is a call to the Medical Image Computing community. When submitting a grant application to the NIH, please include the term '''Medical Image Computing''' in your grant summary and the keywords. You don't need to do anything else, just add the term. If, as a community, we can sustain the volume of applications that are labeled like this, then we can lobby for the process of study section formation to begin.

Our goal is to use the existing policies and governance to create a completely valid avenue for Medical Image Computing grant applications to compete against others in this field on equal footing and for the NIH reviewers to select the best science and engineering in this field with the highest potential to improve medical care. This will improve the reviewing system relative to the current state where Medical Image Computing applications are sent to a variety of different study sections and are evaluated by reviewers with core competences largely outside the MIC field.

=Actions Requested=
* Please include '''Medical Image Computing''' in the summary and keywords of all your future grant applications to NIH.
* Review the wikipedia page on [http://en.wikipedia.org/wiki/Medical_image_computing Medical Image Computing]. Improve the wikipedia page by editing and adding content.

=Signed=
#[http://www.spl.harvard.edu/pages/People/kikinis Ron Kikinis]
#[http://www.martinstyner.org Martin Styner]
#[http://www.sci.utah.edu/people/gerig.html Guido Gerig]
#[http://www.na-mic.org/Wiki/index.php/Algorithm:BU Allen Tannenbaum]
#[http://lmi.bwh.harvard.edu/~westin Carl-Fredrik Westin]

Call for Identification of Medical Image Computing Grant Applications

2012-09-04T08:44:27Z

Westin: /* Signed */

Call for Identification of Medical Image Computing Grant Applications

2012-09-04T08:43:44Z

Westin: /* Signed */

Call for Identification of Medical Image Computing Grant Applications

2012-08-31T10:14:41Z

Westin:

Many of us work in a clinically relevant area of [http://en.wikipedia.org/wiki/Medical_image_computing Medical Image Computing]. And yet, we struggle to get NIH funding for methodological developments in this area of research. While we get encouragement from the program officers, the applications often gets poor scores from the study sections. To the best of our knowledge, there is no study section at NIH on [http://en.wikipedia.org/wiki/Medical_image_computing Medical Image Computing] (rather than [http://en.wikipedia.org/wiki/Medical_imaging Medical Imaging], for example, that is much more focused on improving image acquisition).

To create such study section, NIH needs evidence that enough grants on the topic are submitted to warrant an ad-hoc study section first, which can later be converted to a permanent study section if the stream of applications is sustained over several application cycles.

This is a call for everyone who is submitting a grant application to NIH. Please mention the term Medical Image Computing in your grant summary/abstract and keywords. You don't need to do anything else differently, just add the keyword to the text. After 3-4 grant submission cycles, this will give NIH enough evidence (by searching the applications) to warrant a special ad-hoc study section on Medical Image Computing. This will be a start. If as a community, we can sustain the volume of applications for a separate section, the ad-hoc study section will convert to a permanent one.

We are not calling for any drastic changes in the structure of the NIH review process. Our goal is to use the existing structure to create a completely valid avenue for Medical Image Computing grant applications to compete against others in this field on equal footing and for the NIH reviewers to select the best science and engineering in this field with the highest potential to improve medical care. This will improve the system relative to the current state where Medical Image Computing applications are sent to many other study sections and are evaluated by reviewers largely outside the field.

All you need to do to help with the process is to mention [http://en.wikipedia.org/wiki/Medical_image_computing Medical Image Computing] in the summary and the keywords of the next grant applications you are sending to NIH. And help us improve the
visibility of the field by editing the wikipedia page on [http://en.wikipedia.org/wiki/Medical_image_computing Medical Image Computing].

2012 Winter Project Week

2011-12-06T22:47:57Z

Westin: /* Traumatic Brain Injury DBP */

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==

===IGT===
*MR guided laser ablation for neurosurgery (Dan Orringer, MD BWH, Jason Stafford, MD Anderson, Isaiah Norton BWH)
*Pelvic Registration (Sandy Wells, Firdaus Janoos, Mehdi Moradi UBC/BWH, jan egger, andrey fedorov)
*OpenIGTLink interface for Slicer4(Junichi, Clif Burdette/Jack Blevins, Tamas Ungi, Andras Lasso)
*Needle tracking (atushi yamada, radhika tibrewal, a needle navigation person)
*?mr susceptability (clare poynton, mr physics person?)
* [[2012_Winter_Project_Week:LiveUltrasound|Live ultrasound in Slicer4 using Plus and OpenIGTLink]] (Tamas Ungi, Elvis Chen)
* 4D Ultrasound (Laurent, Noby)

===Traumatic Brain Injury DBP===

* [[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 DBP===
* [[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 DBP===
* [[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 DBP===
* A patch-based approach to the segmentation of organs of risk (Christian Wachinger, Polina Golland)

===Radiation therapy===
* [[2012_Winter_Project_Week:RTTools|RT tools for Slicer4]] (Csaba Pinter, Kevin Wang, Andras Lasso, Greg Sharp)

===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)

===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 extension: 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)
*[[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)
* 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)

=== 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...

Event:2011-Registration-Retreat-Tuesday

2011-02-22T18:30:36Z

Westin: /* = Unsorted Notes */

[[Event:2011_Registration_Retreat| Back to Registration Brainstorming 2011]]

=Tuesday registration topics=

==1 Grand challenge in registration==

The motivation for having a grand challenge in registration is to define a problem where current technology fails, and that is interesting for the community to work on. This will likely lead to novel and relevant solutions. A grand challenge will likely have larger impact than more traditional short term contests that aims at finding the best method available with current technology and careful settings of algorithmic parameters. The task will be to register data sets that are complex enough to force new technology to be developed. Example of grand challenges from other communities, such as for example the vision community is the [http://www.darpa.mil/grandchallenge/overview.asp DARPA Grand Challenges] and the [http://www.computer.org/portal/web/csdl/doi/10.1109/CVPR.2005.268 Face Recognition Grand Challenge]

=== Possible Challenges ===

Challenge should be '''motivated by clinical problem''' and '''solution should use standard clinical imaging''' data, not specialized acquisitions.

To prevent "cheating" (more likely: over-training):
* Keep test data secret.
* Run software entries on organizer system using secret data.

Consider multi-phase challenges:
# Phase I: run on any hardware; no resource constraints.
# Phase II: run on commodity hardware, run time and memory constraints.

=== Longitudinal Change Detection ===

Provide baseline and follow-up images (e.g., non-contrast abdominal CT) for a set of cases.

Task: For each case, detect and name difference between anatomies using a pre-defined vocabulary. For each case, the answer is zero (for control cases) or more statements "Structure+Change" where
# "Structure" is one of the following (could use subset of [http://www.radlex.org/ RadLex]:
## Heart
## Lung left
## Lung right
## Spleen
## etc.
# "Change" is one of the following:
## Missing
## Appeared
## VolumeIncreased
## VolumeDecreased
## Lesion appeared

Pros:
# Easy and efficient to produce validation standard (does not require identification of large numbers of landmarks)
# Marketable as an important, "grand" challenge

Cons:
# Solution may not actually involve registration

=== Multi-modality Diagnosis ===

For a number of cases, provide a collection of images of various modalities, e.g., CT with and without CE, PET, MRI, US.

Task: Produce diagnosis for each case.

Preferred are conditions where clinicians are currently unable to diagnose based on imaging, but invasive diagnostic procedure are available that can generate a ground truth (e.g., diffuse heart conditions).

Pros:
# Marketable as important, "grand" challenge

Cons:
# Solution may not actually involve registration.

=== Intra-subject Registration ===

Provide baseline and follow-up date in a given modality (e.g., CT). Hard-to-register data, e.g., whole-body or abdominal.

Task: compute a dense deformation between baseline and follow-up images

Pros:
# Most directly related to registration.

Cons:
# Validation is not straight forward. Could involve prospective or intrinsic landmarks, multi-channel acquisition with landmarks visible in a "secret" image channel.

Gold Standard needed for comparing challenge entries:
* Prospective using artifical markers: Markers should be airbrushed in the images provided to participants, similar to Vanderbilt data set.
* Anatomical landmarks: set of landmarks should be large/dense and be kept secret from participants.
* Extracted features (e.g., white matter sheets, skeletons): need to be careful, because while these may be invariant in one subject over time, they may not be comparable across subjects. Consequently, more landmarks should be available for longitudinal than inter-subject registration, because invariant features exist (e.g., in breast, prostate) that do not exist across subjects.
* Contrast-enhanced images could be used to obtain (e.g., vascular) landmarks for validation, but non-contrast images provided to challenge participants.
* Different types of landmarks include:
*# bones, bone features, tips
*# vascular structures, e.g., branching points
*# organs and features on their surface

Interpolation vs. Extrapolation:
* At visible landmarks, their alignment measures registration accuracy directly (modeling, interpolation).
* At invisible landmarks, alignment measures performance of the registration priors (prediction, extrapolation).

===Unsorted Notes===
* Example of such a data is full body registration, with for example data from mice CT.
* One issue that comes up in a grand challenge is how to define goodness/success. How do we define what is a good registration?
* Vanderbilt data set. Blind evaluation and “you cheat you lose” approach.
* Look at taxonomy, see what checks off: if speed is important, if ...
* Use a clinical outcome for the quality of the result? Use a secondary system, that relies on the registration to make its decision.
* Subjective clinical decisions often not reliable (example size of ventricles, normal, enlarged, hugely enlarged)
* Several grand challenges, for example estimate the uncertainty of the registration.
* What can today’s method to well? Good start to find a grand challenge.
* Pig, 1000 lead balls. CT the Pig, move, CT again. Do radiation therapy. Shrink tumors. etc.
* Need a grant to get such a project going.
* The balls migrate over time, can we use anatomical landmarks. Can we use features in the data for landmarks that also will be used for driving the algorithm?
* Error bars on positions of landmarks.
* Find landmarks, easier in bone, vascularture, gyration patterns, more difficult with breast, and in white matter.
* Using anatomical feature for registration often robust (vasculature, ..).
* Define validation strategies that most people agree on, but is strongly related to the applications.
* What is the aspect, robustness, accuracy, speed? Need to be specific in a challenge.
* Two types of registration, having visual landmarks, or not. If no visible features, still models of stiffness and physical properties can meaningfully predict movement.
* Point landmarks, Synthetic data, what are the taxonomy for metrics?
* Other user (metrics?) critera are: is it to slow, is it useful? Amount of user interaction, etc.
* Marketing, grant challenge should capture imagination, should not be technology oriented. A vision that can capture attention, and funding.
* Come up with a medically relevant topic.
* Asking clinicians if this is good enough, get to the relevance. Then ask the practical questions, is this fast enough, robust enough, ...

==2 What works using current technology==

Draft Word document: [[Media:What works.docx|What works]]

==3 White paper outline==

Event:2011-Registration-Retreat-Tuesday

2011-02-22T18:30:16Z

Westin: /* Notes */

[[Event:2011_Registration_Retreat| Back to Registration Brainstorming 2011]]

=Tuesday registration topics=

==1 Grand challenge in registration==

The motivation for having a grand challenge in registration is to define a problem where current technology fails, and that is interesting for the community to work on. This will likely lead to novel and relevant solutions. A grand challenge will likely have larger impact than more traditional short term contests that aims at finding the best method available with current technology and careful settings of algorithmic parameters. The task will be to register data sets that are complex enough to force new technology to be developed. Example of grand challenges from other communities, such as for example the vision community is the [http://www.darpa.mil/grandchallenge/overview.asp DARPA Grand Challenges] and the [http://www.computer.org/portal/web/csdl/doi/10.1109/CVPR.2005.268 Face Recognition Grand Challenge]

=== Possible Challenges ===

Challenge should be '''motivated by clinical problem''' and '''solution should use standard clinical imaging''' data, not specialized acquisitions.

To prevent "cheating" (more likely: over-training):
* Keep test data secret.
* Run software entries on organizer system using secret data.

Consider multi-phase challenges:
# Phase I: run on any hardware; no resource constraints.
# Phase II: run on commodity hardware, run time and memory constraints.

=== Longitudinal Change Detection ===

Provide baseline and follow-up images (e.g., non-contrast abdominal CT) for a set of cases.

Task: For each case, detect and name difference between anatomies using a pre-defined vocabulary. For each case, the answer is zero (for control cases) or more statements "Structure+Change" where
# "Structure" is one of the following (could use subset of [http://www.radlex.org/ RadLex]:
## Heart
## Lung left
## Lung right
## Spleen
## etc.
# "Change" is one of the following:
## Missing
## Appeared
## VolumeIncreased
## VolumeDecreased
## Lesion appeared

Pros:
# Easy and efficient to produce validation standard (does not require identification of large numbers of landmarks)
# Marketable as an important, "grand" challenge

Cons:
# Solution may not actually involve registration

=== Multi-modality Diagnosis ===

For a number of cases, provide a collection of images of various modalities, e.g., CT with and without CE, PET, MRI, US.

Task: Produce diagnosis for each case.

Preferred are conditions where clinicians are currently unable to diagnose based on imaging, but invasive diagnostic procedure are available that can generate a ground truth (e.g., diffuse heart conditions).

Pros:
# Marketable as important, "grand" challenge

Cons:
# Solution may not actually involve registration.

=== Intra-subject Registration ===

Provide baseline and follow-up date in a given modality (e.g., CT). Hard-to-register data, e.g., whole-body or abdominal.

Task: compute a dense deformation between baseline and follow-up images

Pros:
# Most directly related to registration.

Cons:
# Validation is not straight forward. Could involve prospective or intrinsic landmarks, multi-channel acquisition with landmarks visible in a "secret" image channel.

Gold Standard needed for comparing challenge entries:
* Prospective using artifical markers: Markers should be airbrushed in the images provided to participants, similar to Vanderbilt data set.
* Anatomical landmarks: set of landmarks should be large/dense and be kept secret from participants.
* Extracted features (e.g., white matter sheets, skeletons): need to be careful, because while these may be invariant in one subject over time, they may not be comparable across subjects. Consequently, more landmarks should be available for longitudinal than inter-subject registration, because invariant features exist (e.g., in breast, prostate) that do not exist across subjects.
* Contrast-enhanced images could be used to obtain (e.g., vascular) landmarks for validation, but non-contrast images provided to challenge participants.
* Different types of landmarks include:
*# bones, bone features, tips
*# vascular structures, e.g., branching points
*# organs and features on their surface

Interpolation vs. Extrapolation:
* At visible landmarks, their alignment measures registration accuracy directly (modeling, interpolation).
* At invisible landmarks, alignment measures performance of the registration priors (prediction, extrapolation).

=== Unsorted Notes==
* Example of such a data is full body registration, with for example data from mice CT.
* One issue that comes up in a grand challenge is how to define goodness/success. How do we define what is a good registration?
* Vanderbilt data set. Blind evaluation and “you cheat you lose” approach.
* Look at taxonomy, see what checks off: if speed is important, if ...
* Use a clinical outcome for the quality of the result? Use a secondary system, that relies on the registration to make its decision.
* Subjective clinical decisions often not reliable (example size of ventricles, normal, enlarged, hugely enlarged)
* Several grand challenges, for example estimate the uncertainty of the registration.
* What can today’s method to well? Good start to find a grand challenge.
* Pig, 1000 lead balls. CT the Pig, move, CT again. Do radiation therapy. Shrink tumors. etc.
* Need a grant to get such a project going.
* The balls migrate over time, can we use anatomical landmarks. Can we use features in the data for landmarks that also will be used for driving the algorithm?
* Error bars on positions of landmarks.
* Find landmarks, easier in bone, vascularture, gyration patterns, more difficult with breast, and in white matter.
* Using anatomical feature for registration often robust (vasculature, ..).
* Define validation strategies that most people agree on, but is strongly related to the applications.
* What is the aspect, robustness, accuracy, speed? Need to be specific in a challenge.
* Two types of registration, having visual landmarks, or not. If no visible features, still models of stiffness and physical properties can meaningfully predict movement.
* Point landmarks, Synthetic data, what are the taxonomy for metrics?
* Other user (metrics?) critera are: is it to slow, is it useful? Amount of user interaction, etc.
* Marketing, grant challenge should capture imagination, should not be technology oriented. A vision that can capture attention, and funding.
* Come up with a medically relevant topic.
* Asking clinicians if this is good enough, get to the relevance. Then ask the practical questions, is this fast enough, robust enough, ...

==2 What works using current technology==

Draft Word document: [[Media:What works.docx|What works]]

==3 White paper outline==

Event:2011-Registration-Retreat-Tuesday

2011-02-22T18:29:09Z