[Page under construction]

=Introduction=
The 3D Slicer UNC workshop is a full day course that will combine a series of lectures and hands-on sessions using the 3D Slicer software. The morning session will provide an introduction to the basics of data loading, 3D visualization and image registration. The afternoon session will focus on fundamental and practical aspects of Diffusion MRI analysis.

=Workshop Organizers=
* Sonia Pujol, Ph.D., Brigham and Women's Hospital, Harvard Medical School
* Martin Styner, Ph.D., University of North Carolina at Chapel Hill

=Local Host=
* Martin Styner, Ph.D., University of North Carolina at Chapel Hill

=Tentative Agenda=
*9:00-9:15 am Computer setup assistance by the instructors
*9:15-9:30 am Welcome and Goals of the Workshop (Martin Styner)
*9:30-10:00 am Introduction to the 3D Slicer software and community (Sonia Pujol)
*10:00-11:00 am Hands-on session 1: 3D Data Loading and Visualization (partI) (Sonia Pujol)
*11:00-11:15 am Coffee break
*11:15-12:15 pm Hands on session2: Image registration (Sonia Pujol)
*12:15-1:30 pm Lunch
*1:30 pm-2:15 pm An Introduction to Diffusion Tensor Imaging (Sonia Pujol)
*2:15 pm-2:45 pm The DTI Prep Tool (Martin Styner/Francois Budin)
*2:45 pm -3:00 pm Coffee Break
*3:00-3:45 pm White Matter exploration for Neurosurgical Planning (Sonia Pujol)
*3:45-4:30 pm 3D Slicer UNC demos (Martin Styner/Francois Budin) using DTI-Reg, FiberViewerLight and DTIAtlasFiberAnalyzer
*4:30-5:00 pm Questions from the audience and concluding remarks

=Logistics=
* Date: Thursday June 11, 2015
* Location: Brooks Hall, FB 141
* Workshop Materials: The workshop will be a series of hands-on sessions using Slicer 4.4 release version (http://slicer.org) and the following datasets:
**[[media:Slicer3VisualizationDataset.zip | 3D Visualization Dataset]]
**[[ Diffusion Dataset]]
**[[Media:WhiteMatterExplorationData.zip|White Matter Exploration dataset]].
* Registration: To register to the event, please fill in the [[Media:RegistrationForm_SlicerWorkshop_UNC2015.doc‎| UNC registration form]] and send it to Alden Sharpe (ajsharpe at cs.unc.edu).

UNC 2015

2015-05-18T16:46:25Z

Styner: /* Logistics */

Algorithm:UNC:Shape Analysis

2015-03-06T22:08:08Z

Styner: /* Software */

This page is an older summary version of the pages linked at the [[ Algorithm:UNC | UNC algorithms page]] and the [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM | SPHARM PDM framework page]].

== Description ==

'''Shape''' '''analysis''' has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This project focuses on developing novel methodology and a comprehensive set of tools for the computation of 3D structural statistical '''shape''' '''analysis'''. There are several open problems in this area, ranging from multi-object '''analysis''', enhanced '''shape''' correspondence to statistical '''analysis''' of '''shape''' with clinical covariates. We at '''UNC''' have developed and implemented an ITK-based framework for local '''shape''' '''analysis'''. Several projects handle specific visualizations, correspondence establishing methodology and sophisticated statistics. All of these projects have a generic component that make relevant in other applications (e.g. the statistical methods can be applied to other settings in need of multiple comparison correction, such as when many volumetric measurements are analyzed jointly).

== SPHARM-PDM ==

[[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|thumb|right|300px|]]

The '''UNC''' '''shape''' '''analysis''' is based on an '''analysis''' framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. In summary, the input of the proposed '''shape''' '''analysis''' is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are first processed to fill any interior holes and a minimal smoothing operation. The processed binary segmentations are converted to surface meshes, and a spherical parametrization is computed for the surface meshes using a area-preserving, distortion minimizing spherical mapping. The SPHARM description is computed from the mesh and its spherical parametrization. Using the first order ellipsoid from the spherical harmonic coefficients, the spherical parametrizations are aligned to establish correspondence across all surfaces. The SPHARM description is then sampled into a triangulated surfaces (SPHARM-PDM) via icosahedron subdivision of the spherical parametrization. These SPHARM-PDM surfaces are all spatially aligned using rigid Procrustes alignment. Group differences between groups of surfaces are computed using the standard robust Hotelling T 2 two sample metric (see below). Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information.

The implementation has reached a stable framework and has been disseminated to several collaborating labs within NAMIC (BWH, GeorgiaTech, Utah). The current development focuses on integrating the current command line tools into the Slicer (v3) via the Slicer execution model.

== Correspondence ==

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

The SPHARM-PDM based correspondence is a global correspondence method that does performs well for many structures. But in our studies it has shown to be inferior to population based correspondence methods, when assessing statistical modeling properties derived from the established correspondence, such as specificity and generalization ability of a statistical model. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have extended the population based correspondence framework to include curvature based measurements, such as the Koenderink '''Shape''' Index S and Curvedness C in combination with the standard location information. The implementation is based on ITK and uses the SPHARM-PDM correspondence as an initialization. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach. Especially with more complex structures, such as the femural bone and the striatal structure (composed of caudate, nucleus accumbens and putamen), our method outperforms the other methods. It also illustrates the potential of this approach of objects as complex as the human cortex, the object of study in the NAMIC year 07/08.

== Statistics ==

[[Image:UNCShape_CaudatePval_MICCAI06.png|thumb|right|300px|]]

In our statistical framework, group differences between groups of surfaces are computed locally for every surface point using the standard robust Hotelling T^2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information.

The local '''shape''' '''analysis''' involves testing from a few to many thousands of hypothesis (one per surface ele- ment) for statistically significant effects. It is thus important to control for the multiple testing problem, and the most common measure of multiple false positives is the familywise error rate (FWER). The multiple testing problem has been an active area of research in the functional neuroimaging community. One of the widely used methods in the '''analysis''' of neuroimaging data makes inferences based on the maximum distribution. In our work, we are employing non-parametric permutation tests to estimate uniformly sensitive extrema distributions. The correction method is based on computing first the local p-values using permutation tests. The minimum of these p-values across the surface is then computed for every permutation. The appropriate corrected p-value at level α can then be obtained by the computing the value at the α-quantile in the histogram of these minimum values. Using the minimum statistic of the p-values, this method correctly controls for the FWER, or the false positives, but no control of the false negatives is provided. The resulting corrected local significance values can thus be regarded as pessimistic estimates akin to a simple Bonferroni correctection.

Additionally to the non-parametric permutation correction, we have also implemented and applied a False Discovery Rate Estimation (FDR) method. The innovation of this procedure is that it controls the expected proportion of false positives only among those tests for which a local significance has been detected. The FDR method thus allows an expected proportion (usually 5%) of the FDR corrected significance values to be falsely positive. The correction using FDR provides an interpretable and adaptive criterion with higher power than the non-parametric permutation tests. FDR thus results in a less conservative estimate of the false-negatives.

Current development is extending the framework to include subject variables, such as gender, age and clinical scores into the current pure group difference computation.

== Slicer integration ==

A considerable amount of work was spent on the development aspect of our shape analysis tools. The main visualization tool, KWMeshVisu, can be called directly from Slicer 3 for the overlay of scalar, vector and ellipsoid data onto surfaces (so-called attribution) and the application of a set of versatile colormaps. The display of a saved "attributed" surfaces is then again possible within Slicer 3 to close the loop. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool.

The individual shape analysis components have also been integrated into Slicer 3 modules. While it is entirely possible to run all steps of our shape analysis pipeline by calling the individual modules, this is highly inefficient. As a result we are developing, and a first prototype is ready, a separate shape pipeline tool to called from within Slicer3. This tool creates pipeline scripts for Batchmake, another NAMIC supported project at Kitware, to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline thus becomes entirely encapsulated and accessible to the trained clinical collaborator.

=== Current status ===
In order to add the UNC shape analysis tools to the current 3D Slicer distribution do the following:
# Download Slicer (e.g. version 4.4.0)
# Start the extension manager and download the SPHARM-PDM and ShapePopulationViewer extensions
# restart Slicer
# all individual steps can be run now without problem (they are listed under the Shape Analysis category)

== Software ==

* SPHARM-PDM based framework for '''shape''' '''analysis''' is also available via [http://www.nitrc.org/projects/spharm-pdm/CVS NITRC]

== Publications ==
* Styner M, Xu SC, El-Sayed M, Gerig G, Correspondence Evaluation in Local Shape Analysis and Structural Subdivision, IEEE Symposium on Biomedical Imaging ISBI 2007, in print
* Zhou C, Park DC, Styner M, Wang YM, ROI Constrained Statistical Surface Morphometry, IEEE Symposium on Biomedical Imaging ISBI 2007, in print
* Nain D, Styner M, Niethammer M, Levitt JJ, Shenton ME, Gerig G, Bobick A, Tannenbaum A, Statistical Shape Analysis of Brain Structures Using Spherical Wavelets, IEEE Symposium on Biomedical Imaging ISBI 2007, in print
* M. Styner, I. Oguz, S. Xu, D. Pantazis, and G. Gerig. Statistical group differences in anatomical shape analysis using hotelling T2 metric. Proc SPIE Medical Imaging Conference, in print, 2007.
* C Cascio, M Styner, RG Smith, M Poe, G Gerig, H Hazlett, M Jomier, R Bammer, J Piven, Reduced relationship to cortical white matter revealed by tractography-based segmentation of the corpus callosum in yound children with developmental delay, Am J Psychiatry, 2006, (163) 2157-2163, December.
* M. Styner, I. Oguz, S. Xu, C. Brechbuehler, D. Pantazis, J. Levitt, M. Shenton, G. Gerig. Framework for the Statistical '''Shape''' '''Analysis''' of Brain Structures using SPHARM-PDM. MICCAI 2006 Open Source Workshop. Insight Journal DSpace link: http://hdl.handle.net/1926/215
* I. Oguz, G. Gerig, S. Barre, M. Styner. KWMeshVisu: A Mesh Visualization Tool for '''Shape''' '''Analysis'''. MICCAI 2006 Open Source Workshop. Insight Journal DSpace link: http://hdl.handle.net/1926/220
* Tobias Heimann, I. Oguz, I. Wolf, M. Styner, HP. Meinzer. Implementing the Automatic Generation of 3D Statistical '''Shape''' Models with ITK. MICCAI 2006 Open Source Workshop. Insight Journal DSpace link: http://hdl.handle.net/1926/224
* C Cascio, M. Styner, R. Smith, M. Poe, G. Gerig, H. Hazlett, M. Jomier, R. Bammer, J. Piven: Tractography based segmentation of the corpus callosum reveals reduced callosal size and correlation with cortical white matter in young children with developmental delay, American Journal of Psychiatry, 2006, in press
* M. Styner, M. Jomier, G. Gerig: Closed and Open Source Neuroimage '''Analysis''' Tools and Libraries at '''UNC'''<nowiki>: IEEE Symposium on Biomedical Imaging ISBI 2006, pp. 702-705. </nowiki>
* S. Olmos, M. Bossa, M. Styner: PCA-based filtering for hypothesis testing on hippocampus '''shape''' differences between schizophrenics and normals. Medical Image Understanding and '''Analysis''' 2005, Bristol, pp. 167-170.
* M Styner, JA Lieberman, RK McClure, DR Weingberger, DW Jones, G Gerig: Morphometric '''analysis''' of lateral ventricles in schizophrenia and healthy controls regarding genetic and disease-specific factors, Proceedings of the National Academy of Science, 2005, Vol 102, No. 12, March 29, p 4872-4877.
* D. Pantazis, R.M. Leahy, T.E.Nichols, M. Styner: Statistical Surface Based Morphometry Using a Non-Parametric Approach, IEEE Symposium on Biomedical Imaging ISBI 2004,1283-1286.
* M. Styner, J.A. Lieberman, D. Pantazis, G. Gerig: Boundary and Medial '''Shape''' '''Analysis''' of the Hippocampus in Schizophrenia, Medical Image '''Analysis''', 2004, pp 197-203.
* S.Vetsa, M. Styner, S. Pizer, J. Lieberman, G. Gerig: Caudate '''shape''' discrimination in schizophrenia using template-free non-parametric tests, Medical Image Computing and Computer-Assisted Intervention MICCAI 2003, pp.661-669
* M. Styner, J. Lieberman, G. Gerig: Boundary and Medial '''Shape''' '''Analysis''' of the Hippocampus in Schizophrenia, Medical Image Computing and Computer-Assisted Intervention MICCAI 2003, II, pp. 464-471.
* G. Gerig, K. Muller, E. Kistner, Y. Chi, M. Chakos, M. Styner, J. Lieberman: Age and treatment related local hippocampal changes in schizophrenia explained by a novel '''shape''' '''analysis''' method, Medical Image Computing and Computer-Assisted Intervention MICCAI 2003, II, pp. 651-660.
* M. Styner, G. Gerig, J. Lieberman, D. Jones, D. Weinberger: Statistical '''shape''' '''analysis''' of neuroanatomical structures based on medial models, Medical Image '''Analysis''', 7 (3), 2003, pp. 207-220.

Algorithm:UNC:Shape Analysis

2015-03-06T22:07:17Z

Styner: /* Current status */

This page is an older summary version of the pages linked at the [[ Algorithm:UNC | UNC algorithms page]] and the [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM | SPHARM PDM framework page]].

== Description ==

'''Shape''' '''analysis''' has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This project focuses on developing novel methodology and a comprehensive set of tools for the computation of 3D structural statistical '''shape''' '''analysis'''. There are several open problems in this area, ranging from multi-object '''analysis''', enhanced '''shape''' correspondence to statistical '''analysis''' of '''shape''' with clinical covariates. We at '''UNC''' have developed and implemented an ITK-based framework for local '''shape''' '''analysis'''. Several projects handle specific visualizations, correspondence establishing methodology and sophisticated statistics. All of these projects have a generic component that make relevant in other applications (e.g. the statistical methods can be applied to other settings in need of multiple comparison correction, such as when many volumetric measurements are analyzed jointly).

== SPHARM-PDM ==

[[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|thumb|right|300px|]]

The '''UNC''' '''shape''' '''analysis''' is based on an '''analysis''' framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. In summary, the input of the proposed '''shape''' '''analysis''' is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are first processed to fill any interior holes and a minimal smoothing operation. The processed binary segmentations are converted to surface meshes, and a spherical parametrization is computed for the surface meshes using a area-preserving, distortion minimizing spherical mapping. The SPHARM description is computed from the mesh and its spherical parametrization. Using the first order ellipsoid from the spherical harmonic coefficients, the spherical parametrizations are aligned to establish correspondence across all surfaces. The SPHARM description is then sampled into a triangulated surfaces (SPHARM-PDM) via icosahedron subdivision of the spherical parametrization. These SPHARM-PDM surfaces are all spatially aligned using rigid Procrustes alignment. Group differences between groups of surfaces are computed using the standard robust Hotelling T 2 two sample metric (see below). Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information.

The implementation has reached a stable framework and has been disseminated to several collaborating labs within NAMIC (BWH, GeorgiaTech, Utah). The current development focuses on integrating the current command line tools into the Slicer (v3) via the Slicer execution model.

== Correspondence ==

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

The SPHARM-PDM based correspondence is a global correspondence method that does performs well for many structures. But in our studies it has shown to be inferior to population based correspondence methods, when assessing statistical modeling properties derived from the established correspondence, such as specificity and generalization ability of a statistical model. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have extended the population based correspondence framework to include curvature based measurements, such as the Koenderink '''Shape''' Index S and Curvedness C in combination with the standard location information. The implementation is based on ITK and uses the SPHARM-PDM correspondence as an initialization. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach. Especially with more complex structures, such as the femural bone and the striatal structure (composed of caudate, nucleus accumbens and putamen), our method outperforms the other methods. It also illustrates the potential of this approach of objects as complex as the human cortex, the object of study in the NAMIC year 07/08.

== Statistics ==

[[Image:UNCShape_CaudatePval_MICCAI06.png|thumb|right|300px|]]

In our statistical framework, group differences between groups of surfaces are computed locally for every surface point using the standard robust Hotelling T^2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information.

The local '''shape''' '''analysis''' involves testing from a few to many thousands of hypothesis (one per surface ele- ment) for statistically significant effects. It is thus important to control for the multiple testing problem, and the most common measure of multiple false positives is the familywise error rate (FWER). The multiple testing problem has been an active area of research in the functional neuroimaging community. One of the widely used methods in the '''analysis''' of neuroimaging data makes inferences based on the maximum distribution. In our work, we are employing non-parametric permutation tests to estimate uniformly sensitive extrema distributions. The correction method is based on computing first the local p-values using permutation tests. The minimum of these p-values across the surface is then computed for every permutation. The appropriate corrected p-value at level α can then be obtained by the computing the value at the α-quantile in the histogram of these minimum values. Using the minimum statistic of the p-values, this method correctly controls for the FWER, or the false positives, but no control of the false negatives is provided. The resulting corrected local significance values can thus be regarded as pessimistic estimates akin to a simple Bonferroni correctection.

Additionally to the non-parametric permutation correction, we have also implemented and applied a False Discovery Rate Estimation (FDR) method. The innovation of this procedure is that it controls the expected proportion of false positives only among those tests for which a local significance has been detected. The FDR method thus allows an expected proportion (usually 5%) of the FDR corrected significance values to be falsely positive. The correction using FDR provides an interpretable and adaptive criterion with higher power than the non-parametric permutation tests. FDR thus results in a less conservative estimate of the false-negatives.

Current development is extending the framework to include subject variables, such as gender, age and clinical scores into the current pure group difference computation.

== Slicer integration ==

A considerable amount of work was spent on the development aspect of our shape analysis tools. The main visualization tool, KWMeshVisu, can be called directly from Slicer 3 for the overlay of scalar, vector and ellipsoid data onto surfaces (so-called attribution) and the application of a set of versatile colormaps. The display of a saved "attributed" surfaces is then again possible within Slicer 3 to close the loop. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool.

The individual shape analysis components have also been integrated into Slicer 3 modules. While it is entirely possible to run all steps of our shape analysis pipeline by calling the individual modules, this is highly inefficient. As a result we are developing, and a first prototype is ready, a separate shape pipeline tool to called from within Slicer3. This tool creates pipeline scripts for Batchmake, another NAMIC supported project at Kitware, to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline thus becomes entirely encapsulated and accessible to the trained clinical collaborator.

=== Current status ===
In order to add the UNC shape analysis tools to the current 3D Slicer distribution do the following:
# Download Slicer (e.g. version 4.4.0)
# Start the extension manager and download the SPHARM-PDM and ShapePopulationViewer extensions
# restart Slicer
# all individual steps can be run now without problem (they are listed under the Shape Analysis category)

== Software ==

* SPHARM-PDM based framework for '''shape''' '''analysis''' [http://www.ia.unc.edu/dev/download/shapeAnalysis Binary Download], also available via [http://www.ia.unc.edu/dev/tutorials/Developer/index.htm CVS open source access]
* MDL based correspondence improvement available via [http://www.ia.unc.edu/dev/tutorials/Developer/index.htm CVS open source access]

== Publications ==
* Styner M, Xu SC, El-Sayed M, Gerig G, Correspondence Evaluation in Local Shape Analysis and Structural Subdivision, IEEE Symposium on Biomedical Imaging ISBI 2007, in print
* Zhou C, Park DC, Styner M, Wang YM, ROI Constrained Statistical Surface Morphometry, IEEE Symposium on Biomedical Imaging ISBI 2007, in print
* Nain D, Styner M, Niethammer M, Levitt JJ, Shenton ME, Gerig G, Bobick A, Tannenbaum A, Statistical Shape Analysis of Brain Structures Using Spherical Wavelets, IEEE Symposium on Biomedical Imaging ISBI 2007, in print
* M. Styner, I. Oguz, S. Xu, D. Pantazis, and G. Gerig. Statistical group differences in anatomical shape analysis using hotelling T2 metric. Proc SPIE Medical Imaging Conference, in print, 2007.
* C Cascio, M Styner, RG Smith, M Poe, G Gerig, H Hazlett, M Jomier, R Bammer, J Piven, Reduced relationship to cortical white matter revealed by tractography-based segmentation of the corpus callosum in yound children with developmental delay, Am J Psychiatry, 2006, (163) 2157-2163, December.
* M. Styner, I. Oguz, S. Xu, C. Brechbuehler, D. Pantazis, J. Levitt, M. Shenton, G. Gerig. Framework for the Statistical '''Shape''' '''Analysis''' of Brain Structures using SPHARM-PDM. MICCAI 2006 Open Source Workshop. Insight Journal DSpace link: http://hdl.handle.net/1926/215
* I. Oguz, G. Gerig, S. Barre, M. Styner. KWMeshVisu: A Mesh Visualization Tool for '''Shape''' '''Analysis'''. MICCAI 2006 Open Source Workshop. Insight Journal DSpace link: http://hdl.handle.net/1926/220
* Tobias Heimann, I. Oguz, I. Wolf, M. Styner, HP. Meinzer. Implementing the Automatic Generation of 3D Statistical '''Shape''' Models with ITK. MICCAI 2006 Open Source Workshop. Insight Journal DSpace link: http://hdl.handle.net/1926/224
* C Cascio, M. Styner, R. Smith, M. Poe, G. Gerig, H. Hazlett, M. Jomier, R. Bammer, J. Piven: Tractography based segmentation of the corpus callosum reveals reduced callosal size and correlation with cortical white matter in young children with developmental delay, American Journal of Psychiatry, 2006, in press
* M. Styner, M. Jomier, G. Gerig: Closed and Open Source Neuroimage '''Analysis''' Tools and Libraries at '''UNC'''<nowiki>: IEEE Symposium on Biomedical Imaging ISBI 2006, pp. 702-705. </nowiki>
* S. Olmos, M. Bossa, M. Styner: PCA-based filtering for hypothesis testing on hippocampus '''shape''' differences between schizophrenics and normals. Medical Image Understanding and '''Analysis''' 2005, Bristol, pp. 167-170.
* M Styner, JA Lieberman, RK McClure, DR Weingberger, DW Jones, G Gerig: Morphometric '''analysis''' of lateral ventricles in schizophrenia and healthy controls regarding genetic and disease-specific factors, Proceedings of the National Academy of Science, 2005, Vol 102, No. 12, March 29, p 4872-4877.
* D. Pantazis, R.M. Leahy, T.E.Nichols, M. Styner: Statistical Surface Based Morphometry Using a Non-Parametric Approach, IEEE Symposium on Biomedical Imaging ISBI 2004,1283-1286.
* M. Styner, J.A. Lieberman, D. Pantazis, G. Gerig: Boundary and Medial '''Shape''' '''Analysis''' of the Hippocampus in Schizophrenia, Medical Image '''Analysis''', 2004, pp 197-203.
* S.Vetsa, M. Styner, S. Pizer, J. Lieberman, G. Gerig: Caudate '''shape''' discrimination in schizophrenia using template-free non-parametric tests, Medical Image Computing and Computer-Assisted Intervention MICCAI 2003, pp.661-669
* M. Styner, J. Lieberman, G. Gerig: Boundary and Medial '''Shape''' '''Analysis''' of the Hippocampus in Schizophrenia, Medical Image Computing and Computer-Assisted Intervention MICCAI 2003, II, pp. 464-471.
* G. Gerig, K. Muller, E. Kistner, Y. Chi, M. Chakos, M. Styner, J. Lieberman: Age and treatment related local hippocampal changes in schizophrenia explained by a novel '''shape''' '''analysis''' method, Medical Image Computing and Computer-Assisted Intervention MICCAI 2003, II, pp. 651-660.
* M. Styner, G. Gerig, J. Lieberman, D. Jones, D. Weinberger: Statistical '''shape''' '''analysis''' of neuroanatomical structures based on medial models, Medical Image '''Analysis''', 7 (3), 2003, pp. 207-220.

User:Styner

2014-11-05T15:08:29Z

Styner:

Martin Styner [[mailto:styner@unc.edu styner@unc.edu]]

Core 1: Algorithms, Shape Analysis & DTI Analysis
UNC site coordinator

Address:

Associate Professor
Departments of Computer Science and Psychiatry
Neurodevelopmental Disorders Research Center
University of North Carolina at Chapel Hill
CB 7160
Chapel Hill, NC 27599-7160
Phone: 919 843 1092
Cell: 919 260 6674
email: styner@unc.edu
WWW: http://www.martinstyner.com

User:Styner

2014-11-05T15:08:10Z

Styner:

Martin Styner [[mailto:martin_styner@ieee.org martin_styner@ieee.org]]

Core 1: Algorithms, Shape Analysis & DTI Analysis
UNC site coordinator

Address:

Associate Professor
Departments of Computer Science and Psychiatry
Neurodevelopmental Disorders Research Center
University of North Carolina at Chapel Hill
CB 7160
Chapel Hill, NC 27599-7160
Phone: 919 843 1092
Cell: 919 260 6674
email: styner@unc.edu
WWW: http://www.martinstyner.com

AHM 2014 alg core presentations

2014-01-09T04:05:12Z

Styner: /* Alg core presentations */

== Alg core presentations ==

* Utah 1, Ross Whitaker
* MIT, Polina Golland
* Utah 2, Guido Gerig
* [[media:UNC_AHM2014.pptx‎ | UNC, Martin Styner ]]
* Stony Brook, Allen Tannenbaum

File:UNC AHM2014.pptx

2014-01-09T03:59:01Z

Styner:

AHM 2014 alg core presentations

2014-01-08T23:41:52Z

Styner: Created page with '== Alg core presentations == * Utah 1, Ross Whitaker * MIT, Polina Golland * Utah 2, Guido Gerig * UNC, Martin Styner * Stony Brook, Allen Tannenbaum'

== Alg core presentations ==

* Utah 1, Ross Whitaker
* MIT, Polina Golland
* Utah 2, Guido Gerig
* UNC, Martin Styner
* Stony Brook, Allen Tannenbaum

2014 Winter Project Week DTI Tools Breakout

2014-01-08T06:14:06Z

Styner: /* Related Information */

[[AHM_2014#Agenda|Back to AHM_2014 Agenda]]

==Attendees==
* Martin Styner
* Peter Savadjiev
* Anybody with an interest to use the UNC-Utah NA-MIC atlas based DTI fiber analysis framework (we have to come up with a better & shorter name)

==Background==
DTI has become a crucial modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While several analysis toolsets currently exist (such as SPM & FSL/TBSS), there is a need for a coherent end-to-end toolset that allows an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties.
Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators.

==Presentation==

Martin will present a compressed version of the [[SPIE_2013_DTI_Workshop | SPIE 2013 workshop]] on the framework, leaving out DTI background information, with additional discussions of FiberViewerLight, DTIAtlasFiberAnalyzer and FADDTS (the corresponding matlab based statistical analysis toolbox). In addition, Peter will present tools from the MIT/BWH groups on tract queries, ukf tractography and other dti software.

==Related Information ==

* [https://www.nitrc.org/projects/namicdtifiber/ NITRC Page for the UNC-Utah NA-MIC atlas based DTI fiber analysis framework]
* [[media:FrontiersIBISDTIFramework v7.pdf | PDF of recent submission to Frontiers]]
*[[media:2014-UNC-Utah-NAMIC-Tools.pptx‎ | PPT file for Utah UNC NA-MIC tools]]

File:2014-UNC-Utah-NAMIC-Tools.pptx

2014-01-08T06:12:17Z

Styner:

2014 Winter Project Week DTI Tools Breakout

2014-01-08T01:06:54Z

Styner: /* Attendees */

2014 Winter Project Week DTI Tools Breakout

2014-01-08T00:57:41Z

Styner: /* Presentation */

[[AHM_2014#Agenda|Back to AHM_2014 Agenda]]

==Attendees==
* Martin Styner
* Anybody with an interest to use the UNC-Utah NA-MIC atlas based DTI fiber analysis framework (we have to come up with a better & shorter name)

==Background==
DTI has become a crucial modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While several analysis toolsets currently exist (such as SPM & FSL/TBSS), there is a need for a coherent end-to-end toolset that allows an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties.
Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators.

==Presentation==

Martin will present a compressed version of the [[SPIE_2013_DTI_Workshop | SPIE 2013 workshop]] on the framework, leaving out DTI background information, with additional discussions of FiberViewerLight, DTIAtlasFiberAnalyzer and FADDTS (the corresponding matlab based statistical analysis toolbox). In addition, Peter will present tools from the MIT/BWH groups on tract queries, ukf tractography and other dti software.

==Related Information ==

* [https://www.nitrc.org/projects/namicdtifiber/ NITRC Page for the UNC-Utah NA-MIC atlas based DTI fiber analysis framework]
* [[media:FrontiersIBISDTIFramework v7.pdf | PDF of recent submission to Frontiers]]

2014 How about the Future

2014-01-07T22:19:16Z

Styner: /* Where we go from here */

[[AHM_2014#Agenda|Back to AHM_2014 Agenda]]

{| border="00" cellpadding="5" cellspacing="0" width="90%"
|-
| align="left"|
=The Scoop=
<big><big>
*Project week will continue.
*3D Slicer 4 will continue.
</big></big>
=Introduction=
This page contains talking points for the opening session of the NA-MIC AHM 2014 (the last one).

| style="background: #ebeced" align="center"| [[Image:Slicer-country-stats-2013-11-16.png|500px|Slicer downloads by country and region]]
Slicer downloads by country and region
|-
|}

=What we have accomplished=
*Created an outstanding scientific and engineering community in the field of [http://en.wikipedia.org/wiki/Medical_image_computing Medical Image Computing] (MIC).
*Investigated novel algorithmic approaches: Particle systems, registration algorithms, segmentation algorithms,
*Created the NA-MIC kit, a free open source platform for MIC and the basis for 3D Slicer.
*3D Slicer is today a platform with worldwide impact.

=Highlights=
* Robust algorithms for segmentation in the face of anatomical variability: label fusion
* Comprehensive toolbox for the analysis of individual and population DTI data
* The predominant shape analysis toolbox in the field/used in clinical papers(SPHARM-PDM)
* A novel framework for modeling brain connectivity networks
* Robust pipeline for processing clinical brain images
* Automatic 4D segmentation of longitudinal brain MRI in severe TBI
* NAMIC researchers take lead in novel spatio-temporal image and shape analysis methodologies (Whitaker, Styner, Gerig, Tannenbaum and colleagues)

=Where we go from here=
*Project week will continue.
*3D Slicer 4 will continue.
*The remaining months of NA-MIC funding will be used to simplify the submission of extensions.
*Funding:
**NIH mandated sunset for NA-MIC in June 2014.
**[http://projectreporter.nih.gov/project_info_description.cfm?aid=8415024&icde=18711051&ddparam=&ddvalue=&ddsub=&cr=8&csb=default&cs=ASC NAC] funded through 2018. Ron Kikinis
**[http://projectreporter.nih.gov/project_info_description.cfm?aid=8606944&icde=18711051&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC QIICR] funded through 2018. Ron Kikinis, Andrey Fedorov
** "An Open Source Software for Proton Treatment Planning," NCI/Federal Share Grant C06-CA059267 funded through Dec 2014, Greg Sharp
**[http://projectreporter.nih.gov/project_info_description.cfm?aid=8551816&icde=18802965&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC 4DShape]"4D Shape Analysis for Modeling Spatiotemporal Change Trajectories in Huntington's" (NINDS, 07/01/12- 06/30/15) PIs Gerig/Fletcher Utah, co-investigators Johnson/Paulsen Iowa
** [http://projectreporter.nih.gov/project_info_description.cfm?aid=8576556&icde=18804072 NA-MIC Collaboration R01] (NIDCR) on "Quantification of 3D Bony Changes in Temporomandibular joint osteoarthritis", PI Cevidanes/U Michigan, co-investigators Martin Styner/Beatriz Paniagua/UNC, Steve Piper
**Several other grants are being worked on
*Packages
** "MABS," an end-user software for multi-atlas segmentation, Greg Sharp
** "SPHARM-PDM", an end-user software for shape analysis, also including group-wise based particle correspondence, Martin Styner
** "DTIPrep", Comprehensive DWI/DTI QC tool, Martin Styner
** "DTIAtlasBuilder", DTI co-registration and atlas building software, Martin Styner
** "DTIAtlasFiberAnalyzer", DTI fiber profile statistics generation, Martin Styner
** "FiberViewerLight", DTI fiber processing & clustering software, Martin Styner

2013-09-12T16:14:15Z

Styner: /* Algorithms Core: Slicer Modules Under Development */

= Algorithms Core: Slicer Modules Under Development =
These modules will become (or are already) available as extensions in the Slicer Extension Manager as part of the NAMIC Algorithm Core efforts:

* Left atrium segmentation: In collaboration with the Afib DBP. Graph-cut based segmentation that loads meshes, allows users to interactively "center" the model, perform the graph-cut segmentation, scan convert results into a volumetric format. Contact: Gopal Veni, University of Utah.

* Multimaterial meshing: Surface and volumetric meshing of multimaterial volumes with output surface triangles viewable within Slicer. Contact: Jonathan Bronson, University of Utah.

* Interactive segmentation: Control based interactive segmentation module, allowing users to use feedback and observer based principles to drive active contours to equilibrium position and capture desired features. Contact: Ivan Kolesov, SUNY Stony Brook.

* Sobolev active contours: Robust implementation of the active contour methodology using a Sobolev norm, giving much better results in the presence of noise. Contact: Arie Nakhmani, UAB.

* Model-based RSS for left atrium segmentation: RSS integrated with a shape prior that is specifically desgined for segmentating the left atrium from MR images. Contact: Liangjia Zhu, SUNY Stony Brook.

* Left atrial scar segmentation: Given the endocardium of the left atrium, this module automatically extracts the scarring tissue. Contact: Liangjia Zhu, SUNY Stony Brook.

* DTI Fiber Cleaning & Cropping via FiberViewerLight: Given Slicer fiber tracts, perform semi-automatic cleaning and clustering of fibers, define parametrization planes and crop fibers as necessary. Contact: Francois Budin, UNC

* Alternative diffusion and fiber processing via DTIProcess: Alternative set of tools for diffusion reconstruction, processing, fiber tracking and processing (dtiestim, dtiprocess, fiber track and fiber process, fiberstats (statistics over fiber sets), dtiaverage (averaged over multiple dti images). Contact: Francois Budin, UNC

* DTI Registration and DTI atlas building via DTIAtlasBuilder/DTI-Reg: Unbiased group-wise atlas building including additional refinement step, also provides pari-wise DTI registration tool. Contact: Francois Budin, UNC

* DTI fiber profile extraction & analysis via DTIAtlasFiberAnalyzer: Fiber resampling, profile extraction, statistics gathering and fusion of precomputed attributes, stat results with fibers for visualization. . Contact: Francois Budin, UNC

* SPHARM-PDM & Particle shape correspondence and analysis: From binary segmentations, generate SPHARM-PDM descriptions, updated via the particle-group wise correspondence. Include statistical analysis and QC visualization of many multiple surfaces at the same time. Contact: Francois Budin & Beatriz Panigua, UNC

Algorithm:SlicerModules

2013-09-12T16:03:13Z

Styner: /* Algorithms Core: Slicer Modules Under Development */

SPIE 2013 DTI Workshop

2013-08-30T14:06:56Z

Styner: /* Tentative Agenda */

{| class="wikitable" border="1" cellpadding="8" cellspacing="1"
| colspan="2" style="width:100px" |[[Image:spie2012.gif]]
|-
| style="width:50%" |[[Image:NAMIC.jpg‎]]
| style="width:50%" |[[Image:Logo_nac.gif‎]]
|}
= Exploring Brain Connectivity in-vivo: from Theory to Practice - A hands-on analysis workshop on Diffusion MRI by the National Alliance for Medical Image Computing (NA-MIC) =

'''Course Description''':
[[Image:Spie2012 DTI course spujol.png|500px|right]]

The development of Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) has opened up the possibility of studying the complex organization of the brain's white matter in-vivo. By measuring the diffusion of water molecules in tissues, the technique gives insights into the structure and orientation of major white matter pathways, and DT-MRI findings have the potential to play a critical role in the extraction of meaningful information for diagnosis, prognosis and following of treatment response.
The [http://spie.org/app/program/index.cfm?fuseaction=COURSE&export_id=x12534&ID=x12172&redir=x12172.xml&course_id=E2011888&event_id=896166&programtrack_id=37 SPIE 2013 course] will guide participants through the fundamental aspects of DT-MRI data analysis, as well as the challenges of transferring cutting-edge DT-MRI techniques to clinical routine. The format will include a series of hands-on sessions with the participants running DT-MRI analysis on their own laptops, to provide a practical experience of extracting useful clinical information from Diffusion MR images. The hands-on sessions will use DT-MRI tools from the NA-MIC toolkit, which include the 3DSlicer software, an open-source platform for medical image processing and 3D visualization used in biomedical and clinical research. Participants will be guided through an integrated workflow for exploring the brain white matter in a series of datasets that will be provided as part of the course. This event is part of the on-going effort of the NIH-funded National Alliance for Medical Image Computing (NA-MIC) to transfer the latest advances in biomedical image analysis to the scientific and clinical community.

==Faculty ==
*Sonia Pujol, Ph.D., Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School
*Guido Gerig, Ph.D., The Scientific Computing and Imaging Institute, University of Utah
*Martin Styner, Ph.D.,Neuro Image Research and Analysis Laboratory, University of North Carolina

== Date and Location==
* Saturday February 9, 2013
[http://spie.org/x12166.xml SPIE Medical Imaging 2013, Orlando, Florida].

== Registration==
To register for this course, please visit the [http://spie.org/x12166.xml SPIE 2013 conference website].

== Learning Outcomes ==
This course will enable participants to:
* identify the different components of a DT-MRI fiber tract analysis pipeline
* perform DWI/DTI data quality control
* visualize 3D tensor fields and diffusion-derived maps
* generate 3D reconstructions of white matter tracts in a normal subject and pathological case
* extract and visualize DTI fiber tract profiles
* identify the current challenges inherent in using DT-MRI data in the clinics

For questions related to the workshop, please send an e-mail to Sonia Pujol (spujol at bwh.harvard.edu).

== Intended Audience ==
Scientists, engineers, and clinical researchers who are interested in learning how to use Diffusion Tensor MR Imaging for mapping the white matter of the human brain in health and disease. This course does not need any prior knwoledge, but it can be combined with the course [http://spie.org/app/program/index.cfm?fuseaction=COURSE&export_id=x12534&ID=x12172&redir=x12172.xml&course_id=E0982442&event_id=896165 SC 1063: Diffusion Imaging].

== Tentative Agenda ==

*12:30-1:30 pm Pre-workshop installation of software and datasets: [[Media:Spie2013SC1065UNCSlicerTutorial.pdf | Slides Extension Installation (PDF)]]
*1:30-1:35 pm Introduction to the course (Sonia Pujol)
*1:35-2:00 pm Fundamentals of DTI analysis (Guido Gerig)
*2:00-2:10 pm Presentation of the hands-on DTI pipeline ** (Sonia Pujol)
*2:10-2:40 pm Dicom conversion and DWI Quality Control (Martin Styner): [[Media:2013-SPIE-1-DICOMToNRRDConversionTutorial.pptx‎| Slides Dicom Conversion(PPTX)]] / [[Media:2013-SPIE-2-DTIQC.pptx‎| Slides DTIPrep QC (PPTX) ]]
*2:40-3:10 pm DTI (pairwise) registration for clinical studies (Martin Styner): [[Media:2013-SPIE-3-DTI-Reg-Tutorial.pptx‎ | Slides DTI-Reg (PPTX)]]

*3:10-3:25 pm Coffee Break

*3:25-3:50 pm DTI unbiased atlas building for population studies (Martin Styner) [[Media:2013-SPIE-4-DTIAtlasBuilder_Tutorial.pptx‎ | Slides DTIAtlasBuilder (PPTX)]]
*3:50-4:50 pm [[media:DiffusionTensorImaging SPIE2013 SoniaPujol.pdf | From DWI images to fiber tracts (Sonia Pujol) (Tensor Estimation, Glyphs, Scalar Diffusion Measurements, Fiber Tracking) ]]
*4:50-5:15 pm Towards validation of DTI Tractography (Sonia Pujol)
*5:15-5:30 pm Conclusion and Questions from the audience

* Additional slides:
** FiberViewerLight tutorial ([[media:2012-SPIE-FiberViewerLightTutorial.pptx | slides from 2012]])
** DTI fiber profile processing and analysis via DTIFiberAtlasAnalyzer

==Preparation for the hands-on sessions: Software and datasets ==
The workshop combines oral presentations and instructor-led hands-on sessions with the participants working on their own laptop computers.
All participants are required to come with their own laptop computer and install the software and datasets prior to the event. A minimum of 2 GB of RAM (4 GB is better) and a graphic accelerator with 256 MB (512MB is better) of on-board graphic memory are required.

*The 3DSlicer version 4.2.2.1 software will need to be downloaded from here: [http://download.slicer.org Slicer download page (version r21513)]
*Additional instructions on how to install the DTI processing extensions will be provided next week.
*The datasets to download will be posted a few days before the event on this website.
*In addition, time is allotted to install the data from USB sticks at the beginning of the workshop.

The following OS are supported: MacOS X Lion, Windows 7 (VS 2008), Linux 64.

== Slicer Community ==
Participants are invited to join the [http://www.slicer.org/pages/Mailinglist Slicer user and Slicer developer mailing lists] prior to the workshop. This is a place for the Slicer community to discuss questions and feature requests related to 3D Slicer.

== Slicer4 Training Survey ==
[http://www.surveymonkey.com/s/GZDXKXQ Click here to take the Slicer4 Training Survey]

==NA-MIC Presence at SPIE 2013 ==
* [http://spie.org/Documents/ConferencesExhibitions/MI2013-Final-lr.pdf DTI/Functional session, Sunday Feb.10, 2013 10:10 am -12:10 pm] Session Chair: Sonia Pujol, Ph.D.
* [http://spie.org/Documents/ConferencesExhibitions/MI2013-Final-lr.pdf Temporal and Motional Analysis, Sunday Feb.10, 2013, 3:30 pm -5:30 pm] Session Chair: Martin Styner, Ph.D.

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

SPIE 2013 DTI Workshop

2013-08-30T14:06:34Z

Styner: /* Tentative Agenda */

{| class="wikitable" border="1" cellpadding="8" cellspacing="1"
| colspan="2" style="width:100px" |[[Image:spie2012.gif]]
|-
| style="width:50%" |[[Image:NAMIC.jpg‎]]
| style="width:50%" |[[Image:Logo_nac.gif‎]]
|}
= Exploring Brain Connectivity in-vivo: from Theory to Practice - A hands-on analysis workshop on Diffusion MRI by the National Alliance for Medical Image Computing (NA-MIC) =

'''Course Description''':
[[Image:Spie2012 DTI course spujol.png|500px|right]]

The development of Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) has opened up the possibility of studying the complex organization of the brain's white matter in-vivo. By measuring the diffusion of water molecules in tissues, the technique gives insights into the structure and orientation of major white matter pathways, and DT-MRI findings have the potential to play a critical role in the extraction of meaningful information for diagnosis, prognosis and following of treatment response.
The [http://spie.org/app/program/index.cfm?fuseaction=COURSE&export_id=x12534&ID=x12172&redir=x12172.xml&course_id=E2011888&event_id=896166&programtrack_id=37 SPIE 2013 course] will guide participants through the fundamental aspects of DT-MRI data analysis, as well as the challenges of transferring cutting-edge DT-MRI techniques to clinical routine. The format will include a series of hands-on sessions with the participants running DT-MRI analysis on their own laptops, to provide a practical experience of extracting useful clinical information from Diffusion MR images. The hands-on sessions will use DT-MRI tools from the NA-MIC toolkit, which include the 3DSlicer software, an open-source platform for medical image processing and 3D visualization used in biomedical and clinical research. Participants will be guided through an integrated workflow for exploring the brain white matter in a series of datasets that will be provided as part of the course. This event is part of the on-going effort of the NIH-funded National Alliance for Medical Image Computing (NA-MIC) to transfer the latest advances in biomedical image analysis to the scientific and clinical community.

==Faculty ==
*Sonia Pujol, Ph.D., Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School
*Guido Gerig, Ph.D., The Scientific Computing and Imaging Institute, University of Utah
*Martin Styner, Ph.D.,Neuro Image Research and Analysis Laboratory, University of North Carolina

== Date and Location==
* Saturday February 9, 2013
[http://spie.org/x12166.xml SPIE Medical Imaging 2013, Orlando, Florida].

== Registration==
To register for this course, please visit the [http://spie.org/x12166.xml SPIE 2013 conference website].

== Learning Outcomes ==
This course will enable participants to:
* identify the different components of a DT-MRI fiber tract analysis pipeline
* perform DWI/DTI data quality control
* visualize 3D tensor fields and diffusion-derived maps
* generate 3D reconstructions of white matter tracts in a normal subject and pathological case
* extract and visualize DTI fiber tract profiles
* identify the current challenges inherent in using DT-MRI data in the clinics

For questions related to the workshop, please send an e-mail to Sonia Pujol (spujol at bwh.harvard.edu).

== Intended Audience ==
Scientists, engineers, and clinical researchers who are interested in learning how to use Diffusion Tensor MR Imaging for mapping the white matter of the human brain in health and disease. This course does not need any prior knwoledge, but it can be combined with the course [http://spie.org/app/program/index.cfm?fuseaction=COURSE&export_id=x12534&ID=x12172&redir=x12172.xml&course_id=E0982442&event_id=896165 SC 1063: Diffusion Imaging].

== Tentative Agenda ==

*12:30-1:30 pm Pre-workshop installation of software and datasets: [[Media:Spie2013SC1065UNCSlicerTutorial.pdf | Slides Extension Installation (PDF)]]
*1:30-1:35 pm Introduction to the course (Sonia Pujol)
*1:35-2:00 pm Fundamentals of DTI analysis (Guido Gerig)
*2:00-2:10 pm Presentation of the hands-on DTI pipeline ** (Sonia Pujol)
*2:10-2:40 pm Dicom conversion and DWI Quality Control (Martin Styner): [[Media:2013-SPIE-1-DICOMToNRRDConversionTutorial.pptx‎| Slides Dicom Conversion(PPTX)]] / [[Media:2013-SPIE-2-DTIQC.pptx‎| Slides DTIPrep QC (PPTX) ]]
*2:40-3:10 pm DTI (pairwise) registration for clinical studies (Martin Styner): [[Media:2013-SPIE-3-DTI-Reg-Tutorial.pptx‎ | Slides DTI-Reg (PPTX)]]

*3:10-3:25 pm Coffee Break

*3:25-3:50 pm DTI unbiased atlas building for population studies (Martin Styner) [[Media:2013-SPIE-4-DTIAtlasBuilder_Tutorial.pptx‎ | Slides DTIAtlasBuilder (PPTX)]]
*3:50-4:50 pm [[media:DiffusionTensorImaging SPIE2013 SoniaPujol.pdf | From DWI images to fiber tracts (Sonia Pujol) (Tensor Estimation, Glyphs, Scalar Diffusion Measurements, Fiber Tracking) ]]
*4:50-5:15 pm Towards validation of DTI Tractography (Sonia Pujol)
*5:15-5:30 pm Conclusion and Questions from the audience

* Additional slides:
** FiberViewerLight tutorial ([[media::2012-SPIE-FiberViewerLightTutorial.pptx | slides from 2012]])
** DTI fiber profile processing and analysis via DTIFiberAtlasAnalyzer

==Preparation for the hands-on sessions: Software and datasets ==
The workshop combines oral presentations and instructor-led hands-on sessions with the participants working on their own laptop computers.
All participants are required to come with their own laptop computer and install the software and datasets prior to the event. A minimum of 2 GB of RAM (4 GB is better) and a graphic accelerator with 256 MB (512MB is better) of on-board graphic memory are required.

*The 3DSlicer version 4.2.2.1 software will need to be downloaded from here: [http://download.slicer.org Slicer download page (version r21513)]
*Additional instructions on how to install the DTI processing extensions will be provided next week.
*The datasets to download will be posted a few days before the event on this website.
*In addition, time is allotted to install the data from USB sticks at the beginning of the workshop.

The following OS are supported: MacOS X Lion, Windows 7 (VS 2008), Linux 64.

== Slicer Community ==
Participants are invited to join the [http://www.slicer.org/pages/Mailinglist Slicer user and Slicer developer mailing lists] prior to the workshop. This is a place for the Slicer community to discuss questions and feature requests related to 3D Slicer.

== Slicer4 Training Survey ==
[http://www.surveymonkey.com/s/GZDXKXQ Click here to take the Slicer4 Training Survey]

==NA-MIC Presence at SPIE 2013 ==
* [http://spie.org/Documents/ConferencesExhibitions/MI2013-Final-lr.pdf DTI/Functional session, Sunday Feb.10, 2013 10:10 am -12:10 pm] Session Chair: Sonia Pujol, Ph.D.
* [http://spie.org/Documents/ConferencesExhibitions/MI2013-Final-lr.pdf Temporal and Motional Analysis, Sunday Feb.10, 2013, 3:30 pm -5:30 pm] Session Chair: Martin Styner, Ph.D.

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

SPIE 2013 DTI Workshop

2013-06-17T16:12:35Z

Styner: /* Tentative Agenda */

{| class="wikitable" border="1" cellpadding="8" cellspacing="1"
| colspan="2" style="width:100px" |[[Image:spie2012.gif]]
|-
| style="width:50%" |[[Image:NAMIC.jpg‎]]
| style="width:50%" |[[Image:Logo_nac.gif‎]]
|}
= Exploring Brain Connectivity in-vivo: from Theory to Practice - A hands-on analysis workshop on Diffusion MRI by the National Alliance for Medical Image Computing (NA-MIC) =

'''Course Description''':
[[Image:Spie2012 DTI course spujol.png|500px|right]]

The development of Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) has opened up the possibility of studying the complex organization of the brain's white matter in-vivo. By measuring the diffusion of water molecules in tissues, the technique gives insights into the structure and orientation of major white matter pathways, and DT-MRI findings have the potential to play a critical role in the extraction of meaningful information for diagnosis, prognosis and following of treatment response.
The [http://spie.org/app/program/index.cfm?fuseaction=COURSE&export_id=x12534&ID=x12172&redir=x12172.xml&course_id=E2011888&event_id=896166&programtrack_id=37 SPIE 2013 course] will guide participants through the fundamental aspects of DT-MRI data analysis, as well as the challenges of transferring cutting-edge DT-MRI techniques to clinical routine. The format will include a series of hands-on sessions with the participants running DT-MRI analysis on their own laptops, to provide a practical experience of extracting useful clinical information from Diffusion MR images. The hands-on sessions will use DT-MRI tools from the NA-MIC toolkit, which include the 3DSlicer software, an open-source platform for medical image processing and 3D visualization used in biomedical and clinical research. Participants will be guided through an integrated workflow for exploring the brain white matter in a series of datasets that will be provided as part of the course. This event is part of the on-going effort of the NIH-funded National Alliance for Medical Image Computing (NA-MIC) to transfer the latest advances in biomedical image analysis to the scientific and clinical community.

==Faculty ==
*Sonia Pujol, Ph.D., Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School
*Guido Gerig, Ph.D., The Scientific Computing and Imaging Institute, University of Utah
*Martin Styner, Ph.D.,Neuro Image Research and Analysis Laboratory, University of North Carolina

== Date and Location==
* Saturday February 9, 2013
[http://spie.org/x12166.xml SPIE Medical Imaging 2013, Orlando, Florida].

== Registration==
To register for this course, please visit the [http://spie.org/x12166.xml SPIE 2013 conference website].

== Learning Outcomes ==
This course will enable participants to:
* identify the different components of a DT-MRI fiber tract analysis pipeline
* perform DWI/DTI data quality control
* visualize 3D tensor fields and diffusion-derived maps
* generate 3D reconstructions of white matter tracts in a normal subject and pathological case
* extract and visualize DTI fiber tract profiles
* identify the current challenges inherent in using DT-MRI data in the clinics

For questions related to the workshop, please send an e-mail to Sonia Pujol (spujol at bwh.harvard.edu).

== Intended Audience ==
Scientists, engineers, and clinical researchers who are interested in learning how to use Diffusion Tensor MR Imaging for mapping the white matter of the human brain in health and disease. This course does not need any prior knwoledge, but it can be combined with the course [http://spie.org/app/program/index.cfm?fuseaction=COURSE&export_id=x12534&ID=x12172&redir=x12172.xml&course_id=E0982442&event_id=896165 SC 1063: Diffusion Imaging].

== Tentative Agenda ==

*12:30-1:30 pm Pre-workshop installation of software and datasets: [[Media:Spie2013SC1065UNCSlicerTutorial.pdf | Slides Extension Installation (PDF)]]
*1:30-1:35 pm Introduction to the course (Sonia Pujol)
*1:35-2:00 pm Fundamentals of DTI analysis (Guido Gerig)
*2:00-2:10 pm Presentation of the hands-on DTI pipeline ** (Sonia Pujol)
*2:10-2:40 pm Dicom conversion and DWI Quality Control (Martin Styner): [[Media:2013-SPIE-1-DICOMToNRRDConversionTutorial.pptx‎| Slides Dicom Conversion(PPTX)]] / [[Media:2013-SPIE-2-DTIQC.pptx‎| Slides DTIPrep QC (PPTX) ]]
*2:40-3:10 pm DTI (pairwise) registration for clinical studies (Martin Styner): [[Media:2013-SPIE-3-DTI-Reg-Tutorial.pptx‎ | Slides DTI-Reg (PPTX)]]

*3:10-3:25 pm Coffee Break

*3:25-3:50 pm DTI unbiased atlas building for population studies (Martin Styner) [[Media:2013-SPIE-4-DTIAtlasBuilder_Tutorial.pptx‎ | Slides DTIAtlasBuilder (PPTX)]]
*3:50-4:50 pm [[media:DiffusionTensorImaging SPIE2013 SoniaPujol.pdf | From DWI images to fiber tracts (Sonia Pujol) (Tensor Estimation, Glyphs, Scalar Diffusion Measurements, Fiber Tracking) ]]
*4:50-5:15 pm Towards validation of DTI Tractography (Sonia Pujol)
*5:15-5:30 pm Conclusion and Questions from the audience

* Additional slides:
** FiberViewerLight tutorial
** DTI fiber profile processing and analysis via DTIFiberAtlasAnalyzer

==Preparation for the hands-on sessions: Software and datasets ==
The workshop combines oral presentations and instructor-led hands-on sessions with the participants working on their own laptop computers.
All participants are required to come with their own laptop computer and install the software and datasets prior to the event. A minimum of 2 GB of RAM (4 GB is better) and a graphic accelerator with 256 MB (512MB is better) of on-board graphic memory are required.

*The 3DSlicer version 4.2.2.1 software will need to be downloaded from here: [http://download.slicer.org Slicer download page (version r21513)]
*Additional instructions on how to install the DTI processing extensions will be provided next week.
*The datasets to download will be posted a few days before the event on this website.
*In addition, time is allotted to install the data from USB sticks at the beginning of the workshop.

The following OS are supported: MacOS X Lion, Windows 7 (VS 2008), Linux 64.

== Slicer Community ==
Participants are invited to join the [http://www.slicer.org/pages/Mailinglist Slicer user and Slicer developer mailing lists] prior to the workshop. This is a place for the Slicer community to discuss questions and feature requests related to 3D Slicer.

== Slicer4 Training Survey ==
[http://www.surveymonkey.com/s/GZDXKXQ Click here to take the Slicer4 Training Survey]

==NA-MIC Presence at SPIE 2013 ==
* [http://spie.org/Documents/ConferencesExhibitions/MI2013-Final-lr.pdf DTI/Functional session, Sunday Feb.10, 2013 10:10 am -12:10 pm] Session Chair: Sonia Pujol, Ph.D.
* [http://spie.org/Documents/ConferencesExhibitions/MI2013-Final-lr.pdf Temporal and Motional Analysis, Sunday Feb.10, 2013, 3:30 pm -5:30 pm] Session Chair: Martin Styner, Ph.D.

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

SPIE 2013 DTI Workshop

2013-02-08T19:39:10Z

Styner: /* Tentative Agenda */

{| class="wikitable" border="1" cellpadding="8" cellspacing="1"
| colspan="2" style="width:100px" |[[Image:spie2012.gif]]
|-
| style="width:50%" |[[Image:NAMIC.jpg‎]]
| style="width:50%" |[[Image:Logo_nac.gif‎]]
|}
= Exploring Brain Connectivity in-vivo: from Theory to Practice - A hands-on analysis workshop on Diffusion MRI by the National Alliance for Medical Image Computing (NA-MIC) =

'''Course Description''':
[[Image:Spie2012 DTI course spujol.png|500px|right]]

The development of Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) has opened up the possibility of studying the complex organization of the brain's white matter in-vivo. By measuring the diffusion of water molecules in tissues, the technique gives insights into the structure and orientation of major white matter pathways, and DT-MRI findings have the potential to play a critical role in the extraction of meaningful information for diagnosis, prognosis and following of treatment response.
The [http://spie.org/app/program/index.cfm?fuseaction=COURSE&export_id=x12534&ID=x12172&redir=x12172.xml&course_id=E2011888&event_id=896166&programtrack_id=37 SPIE 2013 course] will guide participants through the fundamental aspects of DT-MRI data analysis, as well as the challenges of transferring cutting-edge DT-MRI techniques to clinical routine. The format will include a series of hands-on sessions with the participants running DT-MRI analysis on their own laptops, to provide a practical experience of extracting useful clinical information from Diffusion MR images. The hands-on sessions will use DT-MRI tools from the NA-MIC toolkit, which include the 3DSlicer software, an open-source platform for medical image processing and 3D visualization used in biomedical and clinical research. Participants will be guided through an integrated workflow for exploring the brain white matter in a series of datasets that will be provided as part of the course. This event is part of the on-going effort of the NIH-funded National Alliance for Medical Image Computing (NA-MIC) to transfer the latest advances in biomedical image analysis to the scientific and clinical community.

==Faculty ==
*Sonia Pujol, Ph.D., Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School
*Guido Gerig, Ph.D., The Scientific Computing and Imaging Institute, University of Utah
*Martin Styner, Ph.D.,Neuro Image Research and Analysis Laboratory, University of North Carolina

== Date and Location==
* Saturday February 9, 2013
[http://spie.org/x12166.xml SPIE Medical Imaging 2013, Orlando, Florida].

== Registration==
To register for this course, please visit the [http://spie.org/x12166.xml SPIE 2013 conference website].

== Learning Outcomes ==
This course will enable participants to:
* identify the different components of a DT-MRI fiber tract analysis pipeline
* perform DWI/DTI data quality control
* visualize 3D tensor fields and diffusion-derived maps
* generate 3D reconstructions of white matter tracts in a normal subject and pathological case
* extract and visualize DTI fiber tract profiles
* identify the current challenges inherent in using DT-MRI data in the clinics

For questions related to the workshop, please send an e-mail to Sonia Pujol (spujol at bwh.harvard.edu).

== Intended Audience ==
Scientists, engineers, and clinical researchers who are interested in learning how to use Diffusion Tensor MR Imaging for mapping the white matter of the human brain in health and disease. This course does not need any prior knwoledge, but it can be combined with the course [http://spie.org/app/program/index.cfm?fuseaction=COURSE&export_id=x12534&ID=x12172&redir=x12172.xml&course_id=E0982442&event_id=896165 SC 1063: Diffusion Imaging].

== Tentative Agenda ==

*12:30-1:30 pm Pre-workshop installation of software and datasets
*1:30-1:35 pm Introduction to the course (Sonia Pujol)
*1:35-2:00 pm Fundamentals of DTI analysis (Guido Gerig)
*2:00-2:10 pm Presentation of the hands-on DTI pipeline ** (Sonia Pujol)
*2:10-2:40 pm Dicom conversion and DWI Quality Control (Martin Styner): [[Media:2013-SPIE-1-DICOMToNRRDConversionTutorial.pptx‎| Slides Dicom Conversion(PPTX)]] / [[2013-SPIE-2-DTIQC.pptx‎| Slides DTIPrep QC (PPTX) ]]
*2:40-3:10 pm DTI (pairwise) registration for clinical studies (Martin Styner) [[Media:2013-SPIE-3-DTI-Reg-Tutorial.pptx‎ | Slides DTI-Reg (PPTX)]]

*3:10-3:25 pm Coffee Break

*3:25-3:50 pm DTI unbiased atlas building for population studies (Martin Styner) (DTIAtlasBuilder)**
*3:50-4:50 pm From DWI images to fiber tracts (Sonia Pujol) (Tensor Estimation, Glyphs, Scalar Diffusion Measurements, Fiber Tracking)
*4:50-5:15 pm Towards validation of DTI Tractography (Sonia Pujol)
*5:15-5:30 pm Conclusion and Questions from the audience

* Additional slides:
** FiberViewerLight tutorial
** DTI fiber profile processing and analysis via DTIFiberAtlasAnalyzer

==Preparation for the hands-on sessions: Software and datasets ==
The workshop combines oral presentations and instructor-led hands-on sessions with the participants working on their own laptop computers.
All participants are required to come with their own laptop computer and install the software and datasets prior to the event. A minimum of 2 GB of RAM (4 GB is better) and a graphic accelerator with 256 MB (512MB is better) of on-board graphic memory are required.

*The 3DSlicer version 4.2.2.1 software will need to be downloaded from here: [http://download.slicer.org Slicer download page (version r21513)]
*Additional instructions on how to install the DTI processing extensions will be provided next week.
*The datasets to download will be posted a few days before the event on this website.
*In addition, time is allotted to install the data from USB sticks at the beginning of the workshop.

The following OS are supported: MacOS X Lion, Windows 7 (VS 2008), Linux 64.

== Slicer Community ==
Participants are invited to join the [http://www.slicer.org/pages/Mailinglist Slicer user and Slicer developer mailing lists] prior to the workshop. This is a place for the Slicer community to discuss questions and feature requests related to 3D Slicer.

== Slicer4 Training Survey ==
[http://www.surveymonkey.com/s/GZDXKXQ Click here to take the Slicer4 Training Survey]

==NA-MIC Presence at SPIE 2013 ==
* [http://spie.org/Documents/ConferencesExhibitions/MI2013-Final-lr.pdf DTI/Functional session, Sunday Feb.10, 2013 10:10 am -12:10 pm] Session Chair: Sonia Pujol, Ph.D.
* [http://spie.org/Documents/ConferencesExhibitions/MI2013-Final-lr.pdf Temporal and Motional Analysis, Sunday Feb.10, 2013, 3:30 pm -5:30 pm] Session Chair: Martin Styner, Ph.D.

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

File:2013-SPIE-3-DTI-Reg-Tutorial.pptx

2013-02-08T19:38:40Z

Styner:

SPIE 2013 DTI Workshop

2013-02-08T19:38:07Z

Styner: /* Tentative Agenda */

{| class="wikitable" border="1" cellpadding="8" cellspacing="1"
| colspan="2" style="width:100px" |[[Image:spie2012.gif]]
|-
| style="width:50%" |[[Image:NAMIC.jpg‎]]
| style="width:50%" |[[Image:Logo_nac.gif‎]]
|}
= Exploring Brain Connectivity in-vivo: from Theory to Practice - A hands-on analysis workshop on Diffusion MRI by the National Alliance for Medical Image Computing (NA-MIC) =

'''Course Description''':
[[Image:Spie2012 DTI course spujol.png|500px|right]]

The development of Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) has opened up the possibility of studying the complex organization of the brain's white matter in-vivo. By measuring the diffusion of water molecules in tissues, the technique gives insights into the structure and orientation of major white matter pathways, and DT-MRI findings have the potential to play a critical role in the extraction of meaningful information for diagnosis, prognosis and following of treatment response.
The [http://spie.org/app/program/index.cfm?fuseaction=COURSE&export_id=x12534&ID=x12172&redir=x12172.xml&course_id=E2011888&event_id=896166&programtrack_id=37 SPIE 2013 course] will guide participants through the fundamental aspects of DT-MRI data analysis, as well as the challenges of transferring cutting-edge DT-MRI techniques to clinical routine. The format will include a series of hands-on sessions with the participants running DT-MRI analysis on their own laptops, to provide a practical experience of extracting useful clinical information from Diffusion MR images. The hands-on sessions will use DT-MRI tools from the NA-MIC toolkit, which include the 3DSlicer software, an open-source platform for medical image processing and 3D visualization used in biomedical and clinical research. Participants will be guided through an integrated workflow for exploring the brain white matter in a series of datasets that will be provided as part of the course. This event is part of the on-going effort of the NIH-funded National Alliance for Medical Image Computing (NA-MIC) to transfer the latest advances in biomedical image analysis to the scientific and clinical community.

==Faculty ==
*Sonia Pujol, Ph.D., Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School
*Guido Gerig, Ph.D., The Scientific Computing and Imaging Institute, University of Utah
*Martin Styner, Ph.D.,Neuro Image Research and Analysis Laboratory, University of North Carolina

== Date and Location==
* Saturday February 9, 2013
[http://spie.org/x12166.xml SPIE Medical Imaging 2013, Orlando, Florida].

== Registration==
To register for this course, please visit the [http://spie.org/x12166.xml SPIE 2013 conference website].

== Learning Outcomes ==
This course will enable participants to:
* identify the different components of a DT-MRI fiber tract analysis pipeline
* perform DWI/DTI data quality control
* visualize 3D tensor fields and diffusion-derived maps
* generate 3D reconstructions of white matter tracts in a normal subject and pathological case
* extract and visualize DTI fiber tract profiles
* identify the current challenges inherent in using DT-MRI data in the clinics

For questions related to the workshop, please send an e-mail to Sonia Pujol (spujol at bwh.harvard.edu).

== Intended Audience ==
Scientists, engineers, and clinical researchers who are interested in learning how to use Diffusion Tensor MR Imaging for mapping the white matter of the human brain in health and disease. This course does not need any prior knwoledge, but it can be combined with the course [http://spie.org/app/program/index.cfm?fuseaction=COURSE&export_id=x12534&ID=x12172&redir=x12172.xml&course_id=E0982442&event_id=896165 SC 1063: Diffusion Imaging].

== Tentative Agenda ==

*12:30-1:30 pm Pre-workshop installation of software and datasets
*1:30-1:35 pm Introduction to the course (Sonia Pujol)
*1:35-2:00 pm Fundamentals of DTI analysis (Guido Gerig)
*2:00-2:10 pm Presentation of the hands-on DTI pipeline ** (Sonia Pujol)
*2:10-2:40 pm Dicom conversion and DWI Quality Control (Martin Styner): [[Media:2013-SPIE-1-DICOMToNRRDConversionTutorial.pptx‎| Slides Dicom Conversion(PPTX)]] / [[2013-SPIE-2-DTIQC.pptx‎| Slides DTIPrep QC (PPTX) ]]
*2:40-3:10 pm DTI (pairwise) registration for clinical studies (Martin Styner) (DTI-Reg)

*3:10-3:25 pm Coffee Break

*3:25-3:50 pm DTI unbiased atlas building for population studies (Martin Styner) (DTIAtlasBuilder)**
*3:50-4:50 pm From DWI images to fiber tracts (Sonia Pujol) (Tensor Estimation, Glyphs, Scalar Diffusion Measurements, Fiber Tracking)
*4:50-5:15 pm Towards validation of DTI Tractography (Sonia Pujol)
*5:15-5:30 pm Conclusion and Questions from the audience

* Additional slides:
** FiberViewerLight tutorial
** DTI fiber profile processing and analysis via DTIFiberAtlasAnalyzer

==Preparation for the hands-on sessions: Software and datasets ==
The workshop combines oral presentations and instructor-led hands-on sessions with the participants working on their own laptop computers.
All participants are required to come with their own laptop computer and install the software and datasets prior to the event. A minimum of 2 GB of RAM (4 GB is better) and a graphic accelerator with 256 MB (512MB is better) of on-board graphic memory are required.

*The 3DSlicer version 4.2.2.1 software will need to be downloaded from here: [http://download.slicer.org Slicer download page (version r21513)]
*Additional instructions on how to install the DTI processing extensions will be provided next week.
*The datasets to download will be posted a few days before the event on this website.
*In addition, time is allotted to install the data from USB sticks at the beginning of the workshop.

The following OS are supported: MacOS X Lion, Windows 7 (VS 2008), Linux 64.

== Slicer Community ==
Participants are invited to join the [http://www.slicer.org/pages/Mailinglist Slicer user and Slicer developer mailing lists] prior to the workshop. This is a place for the Slicer community to discuss questions and feature requests related to 3D Slicer.

== Slicer4 Training Survey ==
[http://www.surveymonkey.com/s/GZDXKXQ Click here to take the Slicer4 Training Survey]

==NA-MIC Presence at SPIE 2013 ==
* [http://spie.org/Documents/ConferencesExhibitions/MI2013-Final-lr.pdf DTI/Functional session, Sunday Feb.10, 2013 10:10 am -12:10 pm] Session Chair: Sonia Pujol, Ph.D.
* [http://spie.org/Documents/ConferencesExhibitions/MI2013-Final-lr.pdf Temporal and Motional Analysis, Sunday Feb.10, 2013, 3:30 pm -5:30 pm] Session Chair: Martin Styner, Ph.D.

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