https://www.na-mic.org/w/api.php?action=feedcontributions&user=Vandymohan&feedformat=atomNAMIC Wiki - User contributions [en]2024-03-29T11:54:53ZUser contributionsMediaWiki 1.33.0https://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentationPopStudy&diff=51937Projects:TubularSurfaceSegmentationPopStudy2010-05-07T15:30:59Z<p>Vandymohan: /* Publications */</p>
<hr />
<div> Back to [[Algorithm:GATech|Georgia Tech Algorithms]]<br />
__NOTOC__<br />
= Group study using the Tubular Surface model =<br />
<br />
= Description =<br />
<br />
We have proposed a new framework for performing group studies on DW-MRI data using the Tubular Surface Model to study white-matter properties. We show that the model facilitates population studies by the natural registration that occurs by the sampling of WM properties along the fiber bundles center-lines. Further, by allowing us to characterize the discrimination ability of local regions of the fiber bundles, the framework allows us to identify the regions that are "affected" by the disorder under study. In our experiments, we have applied the framework to study the Cingulum Bundle towards discriminating Schizophrenia.<br />
<br />
== Some Results<br />
The results below show the visualization of t-statistics with respect to the extracted features, with discrimination ability increasing from green to red. This demonstrates the ability of the framework to visualize the role of different regions of the fiber bundle in Schizophrenia.<br />
* [[Image:GT-PopStudyVis_OnCBs_Case19-View1.jpg | Visualization of T-statistics on Cingulum Bundle surface (View 1)| 300px]] Visualization of T-statistics on Cingulum Bundle surface (View 1)<br />
* [[Image:GT-PopStudyVis_OnCBs_Case19-View2.jpg | Visualization of T-statistics on Cingulum Bundle surface (View 2)| 300px]] Visualization of T-statistics on Cingulum Bundle surface (View 2)<br />
<br />
<br />
= Key Investigators =<br />
<br />
Georgia Tech: Allen Tannenbaum, Vandana Mohan<br />
<br />
BWH: Marek Kubicki<br />
<br />
= Publications =<br />
''In Print''<br />
* [http://www.na-mic.org/publications/pages/display?search=Projects:TubularSurfaceSegmentationPopStudy&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]<br />
<br />
''In Press''<br />
<br />
V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in submission) <br />
<br />
<br />
[[Category: MRI]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentationPopStudy&diff=51936Projects:TubularSurfaceSegmentationPopStudy2010-05-07T15:29:38Z<p>Vandymohan: /* Publications */</p>
<hr />
<div> Back to [[Algorithm:GATech|Georgia Tech Algorithms]]<br />
__NOTOC__<br />
= Group study using the Tubular Surface model =<br />
<br />
= Description =<br />
<br />
We have proposed a new framework for performing group studies on DW-MRI data using the Tubular Surface Model to study white-matter properties. We show that the model facilitates population studies by the natural registration that occurs by the sampling of WM properties along the fiber bundles center-lines. Further, by allowing us to characterize the discrimination ability of local regions of the fiber bundles, the framework allows us to identify the regions that are "affected" by the disorder under study. In our experiments, we have applied the framework to study the Cingulum Bundle towards discriminating Schizophrenia.<br />
<br />
== Some Results<br />
The results below show the visualization of t-statistics with respect to the extracted features, with discrimination ability increasing from green to red. This demonstrates the ability of the framework to visualize the role of different regions of the fiber bundle in Schizophrenia.<br />
* [[Image:GT-PopStudyVis_OnCBs_Case19-View1.jpg | Visualization of T-statistics on Cingulum Bundle surface (View 1)| 300px]] Visualization of T-statistics on Cingulum Bundle surface (View 1)<br />
* [[Image:GT-PopStudyVis_OnCBs_Case19-View2.jpg | Visualization of T-statistics on Cingulum Bundle surface (View 2)| 300px]] Visualization of T-statistics on Cingulum Bundle surface (View 2)<br />
<br />
<br />
= Key Investigators =<br />
<br />
Georgia Tech: Allen Tannenbaum, Vandana Mohan<br />
<br />
BWH: Marek Kubicki<br />
<br />
= Publications =<br />
''In Print''<br />
* [http://www.na-mic.org/publications/pages/display?search=Projects:TubularSurfaceSegmentationPopStudy&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]<br />
<br />
''In Press''<br />
<br />
*V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission). <br />
<br />
<br />
[[Category: MRI]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentationPopStudy&diff=51935Projects:TubularSurfaceSegmentationPopStudy2010-05-07T15:28:09Z<p>Vandymohan: /* Publications */</p>
<hr />
<div> Back to [[Algorithm:GATech|Georgia Tech Algorithms]]<br />
__NOTOC__<br />
= Group study using the Tubular Surface model =<br />
<br />
= Description =<br />
<br />
We have proposed a new framework for performing group studies on DW-MRI data using the Tubular Surface Model to study white-matter properties. We show that the model facilitates population studies by the natural registration that occurs by the sampling of WM properties along the fiber bundles center-lines. Further, by allowing us to characterize the discrimination ability of local regions of the fiber bundles, the framework allows us to identify the regions that are "affected" by the disorder under study. In our experiments, we have applied the framework to study the Cingulum Bundle towards discriminating Schizophrenia.<br />
<br />
== Some Results<br />
The results below show the visualization of t-statistics with respect to the extracted features, with discrimination ability increasing from green to red. This demonstrates the ability of the framework to visualize the role of different regions of the fiber bundle in Schizophrenia.<br />
* [[Image:GT-PopStudyVis_OnCBs_Case19-View1.jpg | Visualization of T-statistics on Cingulum Bundle surface (View 1)| 300px]] Visualization of T-statistics on Cingulum Bundle surface (View 1)<br />
* [[Image:GT-PopStudyVis_OnCBs_Case19-View2.jpg | Visualization of T-statistics on Cingulum Bundle surface (View 2)| 300px]] Visualization of T-statistics on Cingulum Bundle surface (View 2)<br />
<br />
<br />
= Key Investigators =<br />
<br />
Georgia Tech: Allen Tannenbaum, Vandana Mohan<br />
<br />
BWH: Marek Kubicki<br />
<br />
= Publications =<br />
''In Print''<br />
* [http://www.na-mic.org/publications/pages/display?search=Projects%TubularSurfaceSegmentationPopStudy&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]<br />
<br />
''In Press''<br />
<br />
*V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission). <br />
<br />
<br />
[[Category: MRI]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:SegmentationEndocardialWall&diff=51934Projects:SegmentationEndocardialWall2010-05-07T15:22:16Z<p>Vandymohan: /* Publications */</p>
<hr />
<div>Back to [[Algorithm:GATech|Georgia Tech Algorithms]]<br />
__NOTOC__<br />
<gallery><br />
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall<br />
Image:2d_axial_LA.png | 2D View<br />
</gallery><br />
<br />
<br />
= Description =<br />
<br />
Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as ''catheter ablation'', specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.<br />
<br />
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.<br />
<br />
== Our Approach ==<br />
<br />
A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.<br />
<br />
= Publications =<br />
<br />
''In Print''<br />
* [http://www.na-mic.org/publications/pages/display?search=Projects%3ASegmentationEndocardialWall&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database]<br />
<br />
= Key Investigators =<br />
<br />
Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum<br />
<br />
University of Utah: Rob MacLeod, and Josh Blauer</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:SegmentationEndocardialWall&diff=51933Projects:SegmentationEndocardialWall2010-05-07T15:21:29Z<p>Vandymohan: /* Publications */</p>
<hr />
<div>Back to [[Algorithm:GATech|Georgia Tech Algorithms]]<br />
__NOTOC__<br />
<gallery><br />
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall<br />
Image:2d_axial_LA.png | 2D View<br />
</gallery><br />
<br />
<br />
= Description =<br />
<br />
Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as ''catheter ablation'', specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.<br />
<br />
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.<br />
<br />
== Our Approach ==<br />
<br />
A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.<br />
<br />
= Publications =<br />
<br />
''In Print''<br />
* [http://www.na-mic.org/publications/pages/display?search=Projects%3ASegmentationEndocardialWall&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database]<br />
<br />
Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010 [[File:SPIE_MI_Gholami.pdf|link]]<br />
<br />
= Key Investigators =<br />
<br />
Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum<br />
<br />
University of Utah: Rob MacLeod, and Josh Blauer</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentation&diff=51927Projects:TubularSurfaceSegmentation2010-05-07T14:46:51Z<p>Vandymohan: /* Publications */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:GATech|Georgia Tech Algorithms]], [[Engineering:Kitware|Kitware Engineering]]<br />
__NOTOC__<br />
= Tubular Surface Segmentation Framework =<br />
<br />
This is a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. This model affords computation efficiency and stability (by the use of the Sobolev norm) and we have successfully tested its application in segmenting the Cingulum Bundle from DW-MRI of the brain as well as vessel segmentation from CT cardiac data.<br />
<br />
= Description =<br />
<br />
We have proposed a new model for tubular surfaces that represents a tubular surface as a center-line with a radius function associated with every point of the center-line. This transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels.<br />
<br />
== Some Results ==<br />
<br />
* [[Image:GTTubSurfaceSeg-Img1.png | 3D visualization of CB segmentation| 300px]] 3D visualization of CB segmentation<br />
* [[Image:GTTubSurfaceSeg-Slice_Img1.png | Slice-wise view of CB Segmentation result | 300px]] Slice-wise view of CB Segmentation result<br />
* [[Image:GTTubSurfaceSeg-Slice_Img2.png | Slice-wise view of CB Segmentation result | 300px]] Slice-wise view of CB Segmentation result<br />
* [[Image:GT-VesselSegByTubSeg-ResultIn3D-1.jpg | 3D visualization of vessel segmentation | 300px]] 3D View of vessel segmentation result: initial volume (blue), segmentation result (red), extracted center-line (green)<br />
* [[Image:GT-VesselSegByTubSeg-ResultSliceView-1.jpg | Slice-wise visualization of vessel segmentation | 300px]] Slice-wise view of vessel segmentation result: segmentation result (green) and extracted center-line (red) superimposed on CTA image data<br />
<br />
== Project Status ==<br />
* Algorithm successfully implemented and tested in MATLAB for Cingulum Bundle (as well as vessel segmentation).<br />
<br />
= Key Investigators =<br />
<br />
* Georgia Tech Algorithms: Vandana Mohan, Allen Tannenbaum<br />
* Kitware Engineering: Luis Ibanez<br />
<br />
= Publications =<br />
''In Print''<br />
* [http://www.na-mic.org/publications/pages/display?search=Projects%3ATubularSurfaceSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database]<br />
<br />
''In Press''<br />
<br />
V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in review). IEEE Transactions on Medical Imaging (2010).<br />
<br />
<br />
Project Week Results: [[2009_Summer_Project_Week_TubularSurfaceSeg|June 2009]]<br />
<br />
[[Category: Segmentation]] [[Category:MRI]] [[Category:Slicer]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentation&diff=48507Projects:TubularSurfaceSegmentation2010-02-09T03:04:58Z<p>Vandymohan: /* Some Results */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:GATech|Georgia Tech Algorithms]], [[Engineering:Kitware|Kitware Engineering]]<br />
__NOTOC__<br />
= Tubular Surface Segmentation Framework =<br />
<br />
This is a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. This model affords computation efficiency and stability (by the use of the Sobolev norm) and we have successfully tested its application in segmenting the Cingulum Bundle from DW-MRI of the brain as well as vessel segmentation from CT cardiac data.<br />
<br />
= Description =<br />
<br />
We have proposed a new model for tubular surfaces that represents a tubular surface as a center-line with a radius function associated with every point of the center-line. This transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels.<br />
<br />
== Some Results ==<br />
<br />
* [[Image:GTTubSurfaceSeg-Img1.png | 3D visualization of CB segmentation| 300px]] 3D visualization of CB segmentation<br />
* [[Image:GTTubSurfaceSeg-Slice_Img1.png | Slice-wise view of CB Segmentation result | 300px]] Slice-wise view of CB Segmentation result<br />
* [[Image:GTTubSurfaceSeg-Slice_Img2.png | Slice-wise view of CB Segmentation result | 300px]] Slice-wise view of CB Segmentation result<br />
* [[Image:GT-VesselSegByTubSeg-ResultIn3D-1.jpg | 3D visualization of vessel segmentation | 300px]] 3D View of vessel segmentation result: initial volume (blue), segmentation result (red), extracted center-line (green)<br />
* [[Image:GT-VesselSegByTubSeg-ResultSliceView-1.jpg | Slice-wise visualization of vessel segmentation | 300px]] Slice-wise view of vessel segmentation result: segmentation result (green) and extracted center-line (red) superimposed on CTA image data<br />
<br />
== Project Status ==<br />
* Algorithm successfully implemented and tested in MATLAB for Cingulum Bundle (as well as vessel segmentation).<br />
<br />
= Key Investigators =<br />
<br />
* Georgia Tech Algorithms: Vandana Mohan, Allen Tannenbaum<br />
* Kitware Engineering: Luis Ibanez<br />
<br />
= Publications =<br />
''In Print''<br />
* [http://www.na-mic.org/publications/pages/display?search=Projects%3ATubularSurfaceSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database]<br />
<br />
''In Press''<br />
<br />
V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.<br />
<br />
<br />
Project Week Results: [[2009_Summer_Project_Week_TubularSurfaceSeg|June 2009]]<br />
<br />
[[Category: Segmentation]] [[Category:MRI]] [[Category:Slicer]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentation&diff=48506Projects:TubularSurfaceSegmentation2010-02-09T03:04:31Z<p>Vandymohan: /* Some Results */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:GATech|Georgia Tech Algorithms]], [[Engineering:Kitware|Kitware Engineering]]<br />
__NOTOC__<br />
= Tubular Surface Segmentation Framework =<br />
<br />
This is a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. This model affords computation efficiency and stability (by the use of the Sobolev norm) and we have successfully tested its application in segmenting the Cingulum Bundle from DW-MRI of the brain as well as vessel segmentation from CT cardiac data.<br />
<br />
= Description =<br />
<br />
We have proposed a new model for tubular surfaces that represents a tubular surface as a center-line with a radius function associated with every point of the center-line. This transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels.<br />
<br />
== Some Results ==<br />
<br />
* [[Image:GTTubSurfaceSeg-Img1.png | 3D visualization of CB segmentation| 300px]] 3D visualization of CB segmentation<br />
* [[Image:GTTubSurfaceSeg-Slice_Img1.png | Slice-wise view of CB Segmentation result | 300px]] Slice-wise view of CB Segmentation result<br />
* [[Image:GTTubSurfaceSeg-Slice_Img2.png | Slice-wise view of CB Segmentation result | 300px]] Slice-wise view of CB Segmentation result<br />
* [[Image:GT-VesselSegByTubSeg-ResultIn3D-1.jpg | 3D visualization of vessel segmentation | 300px]] 3D View of vessel segmentation result: initial volume (blue), segmentation result (red), extracted center-line (green)<br />
* [[Image:GT-VesselSegByTubSeg-ResultSliceView-1.jpg | Slice-wise visualization of vessel segmentation | 300px]] Slice-wise view of vessel segmentation result: segmentation result (green) superimposed on CTA image data<br />
<br />
== Project Status ==<br />
* Algorithm successfully implemented and tested in MATLAB for Cingulum Bundle (as well as vessel segmentation).<br />
<br />
= Key Investigators =<br />
<br />
* Georgia Tech Algorithms: Vandana Mohan, Allen Tannenbaum<br />
* Kitware Engineering: Luis Ibanez<br />
<br />
= Publications =<br />
''In Print''<br />
* [http://www.na-mic.org/publications/pages/display?search=Projects%3ATubularSurfaceSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database]<br />
<br />
''In Press''<br />
<br />
V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.<br />
<br />
<br />
Project Week Results: [[2009_Summer_Project_Week_TubularSurfaceSeg|June 2009]]<br />
<br />
[[Category: Segmentation]] [[Category:MRI]] [[Category:Slicer]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=File:GT-VesselSegByTubSeg-ResultSliceView-1.jpg&diff=48505File:GT-VesselSegByTubSeg-ResultSliceView-1.jpg2010-02-09T03:03:27Z<p>Vandymohan: </p>
<hr />
<div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentation&diff=48503Projects:TubularSurfaceSegmentation2010-02-09T03:02:37Z<p>Vandymohan: /* Some Results */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:GATech|Georgia Tech Algorithms]], [[Engineering:Kitware|Kitware Engineering]]<br />
__NOTOC__<br />
= Tubular Surface Segmentation Framework =<br />
<br />
This is a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. This model affords computation efficiency and stability (by the use of the Sobolev norm) and we have successfully tested its application in segmenting the Cingulum Bundle from DW-MRI of the brain as well as vessel segmentation from CT cardiac data.<br />
<br />
= Description =<br />
<br />
We have proposed a new model for tubular surfaces that represents a tubular surface as a center-line with a radius function associated with every point of the center-line. This transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels.<br />
<br />
== Some Results ==<br />
<br />
* [[Image:GTTubSurfaceSeg-Img1.png | 3D visualization of CB segmentation| 300px]] 3D visualization of CB segmentation<br />
* [[Image:GTTubSurfaceSeg-Slice_Img1.png | Slice-wise view of CB Segmentation result | 300px]] Slice-wise view of CB Segmentation result<br />
* [[Image:GTTubSurfaceSeg-Slice_Img2.png | Slice-wise view of CB Segmentation result | 300px]] Slice-wise view of CB Segmentation result<br />
* [[Image:GT-VesselSegByTubSeg-ResultIn3D-1.jpg | 3D visualization of vessel segmentation | 300px]] 3D View of vessel segmentation result: initial volume (blue), segmentation result (red), extracted center-line (green)<br />
<br />
== Project Status ==<br />
* Algorithm successfully implemented and tested in MATLAB for Cingulum Bundle (as well as vessel segmentation).<br />
<br />
= Key Investigators =<br />
<br />
* Georgia Tech Algorithms: Vandana Mohan, Allen Tannenbaum<br />
* Kitware Engineering: Luis Ibanez<br />
<br />
= Publications =<br />
''In Print''<br />
* [http://www.na-mic.org/publications/pages/display?search=Projects%3ATubularSurfaceSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database]<br />
<br />
''In Press''<br />
<br />
V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.<br />
<br />
<br />
Project Week Results: [[2009_Summer_Project_Week_TubularSurfaceSeg|June 2009]]<br />
<br />
[[Category: Segmentation]] [[Category:MRI]] [[Category:Slicer]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=File:GT-VesselSegByTubSeg-ResultIn3D-1.jpg&diff=48501File:GT-VesselSegByTubSeg-ResultIn3D-1.jpg2010-02-09T03:01:25Z<p>Vandymohan: Initial volume (blue), segmentation result (red), extracted center-line (green)</p>
<hr />
<div>Initial volume (blue), segmentation result (red), extracted center-line (green)</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Algorithm:GATech&diff=48495Algorithm:GATech2010-02-09T02:54:45Z<p>Vandymohan: /* Tubular Surface Segmentation Framework */</p>
<hr />
<div> Back to [[Algorithm:Main|NA-MIC Algorithms]]<br />
__NOTOC__<br />
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =<br />
<br />
At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.<br />
<br />
= Georgia Tech Projects =<br />
<br />
<br />
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{| cellpadding="10" style="text-align:left;"<br />
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| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]<br />
| style="width:85%" |<br />
<br />
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==<br />
<br />
The ability to detect and measure non-calcified plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]<br />
<br />
<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.<br />
|-<br />
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| | [[Image:3D_Segmentation_LA.png|200px]]<br />
| |<br />
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== [[Projects:SegmentationEndocardialWall|Segmentation of the Endocardial Wall of the Left Atrium]] ==<br />
<br />
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]<br />
<br />
<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.<br />
<br />
|-<br />
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| | [[Image:Pain1.JPG|200px]]<br />
| |<br />
<br />
== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==<br />
<br />
Pain assessment in patients who are unable to verbally<br />
communicate with medical staff is a challenging problem<br />
in patient critical care. The fundamental limitations in sedation<br />
and pain assessment in the intensive care unit (ICU) stem<br />
from subjective assessment criteria, rather than quantifiable,<br />
measurable data for ICU sedation and analgesia. This often<br />
results in poor quality and inconsistent treatment of patient<br />
agitation and pain from nurse to nurse. Recent advancements in<br />
pattern recognition techniques using a relevance vector machine<br />
algorithm can assist medical staff in assessing sedation and pain<br />
by constantly monitoring the patient and providing the clinician<br />
with quantifiable data for ICU sedation. In this paper, we show<br />
that the pain intensity assessment given by a computer classifier<br />
has a strong correlation with the pain intensity assessed by<br />
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]<br />
<br />
<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).<br />
<br />
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|-<br />
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| | [[Image:MultiObjSeg.png|200px|]]<br />
| |<br />
<br />
== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==<br />
<br />
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]<br />
<br />
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|-<br />
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| | [[Image:ShapeBasePstSegSlicer.png|200px|]]<br />
| |<br />
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==<br />
<br />
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.<br />
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]<br />
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==<br />
<br />
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]<br />
<br />
<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).<br />
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|-<br />
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| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]<br />
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==<br />
<br />
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.<br />
<br />
|-<br />
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]<br />
| |<br />
<br />
== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==<br />
<br />
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]<br />
<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.<br />
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|-<br />
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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]<br />
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==<br />
<br />
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]<br />
<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in preparation)<br />
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|-<br />
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| | [[Image:RadOnc HN seg.png|200px]]<br />
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== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==<br />
<br />
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]<br />
<br />
<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).<br />
<br />
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|-<br />
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| | [[Image:Circle seg.PNG|200px|]]<br />
| |<br />
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== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==<br />
<br />
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99<br />
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| | [[Image:ZoomedResultWithModel.png|200px]]<br />
| |<br />
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==<br />
<br />
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.<br />
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| | [[Image:P1_small.png|200px|]]<br />
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==<br />
<br />
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.<br />
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| | [[Image:BasePair3DModel.JPG|200px|]]<br />
| |<br />
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==<br />
<br />
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]<br />
<br />
<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.<br />
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| | [[Image:TruckInitialization.png|200px|]]<br />
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==<br />
<br />
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by<br />
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]<br />
<br />
<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.<br />
<br />
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| | [[Image:Results brain sag.JPG|200px]]<br />
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==<br />
<br />
The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]<br />
<br />
<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.<br />
<br />
<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.<br />
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|-<br />
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| | [[Image:Gatech caudateBands.PNG|200px]]<br />
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==<br />
<br />
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]<br />
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|-<br />
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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]<br />
| |<br />
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==<br />
<br />
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]<br />
<br />
|-<br />
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| | [[Image:Basis membership.png|200px]]<br />
| |<br />
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==<br />
<br />
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]<br />
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|-<br />
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| | [[Image:Dlpfc1.jpg|200px|]]<br />
| |<br />
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]<br />
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|-<br />
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| | [[Image:Striatum1.png|200px|]]<br />
| |<br />
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]<br />
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|-<br />
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| | [[Image:Brain-flat.PNG|200px]]<br />
| |<br />
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== [[Projects:ConformalFlatteningRegistration|Conformal Flattening (inactive)]] ==<br />
<br />
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]<br />
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| | [[Image:Fig1yan.PNG|200px|]]<br />
| |<br />
<br />
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==<br />
<br />
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.<br />
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| | [[Image:Fig67.png|200px|]]<br />
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==<br />
<br />
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]<br />
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|-<br />
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| | [[Image:Stochastic-snake.png|200px|]]<br />
| |<br />
<br />
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==<br />
<br />
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]<br />
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|-<br />
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| | [[Image:GT-SulciOutlining1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==<br />
<br />
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]<br />
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|-<br />
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| | [[Image:Table1.png|200px|]]<br />
| |<br />
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==<br />
<br />
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]<br />
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|-<br />
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| | [[Image:Gatech SlicerModel2.jpg|200px]]<br />
| |<br />
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==<br />
<br />
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]<br />
<br />
|}</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Algorithm:GATech&diff=48474Algorithm:GATech2010-02-08T20:44:45Z<p>Vandymohan: /* Tubular Surface Segmentation Framework */</p>
<hr />
<div> Back to [[Algorithm:Main|NA-MIC Algorithms]]<br />
__NOTOC__<br />
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =<br />
<br />
At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.<br />
<br />
= Georgia Tech Projects =<br />
<br />
<br />
<br />
{| cellpadding="10" style="text-align:left;"<br />
<br />
| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]<br />
| style="width:85%" |<br />
<br />
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==<br />
<br />
The ability to detect and measure non-calcified plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]<br />
<br />
<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.<br />
|-<br />
<br />
| | [[Image:3D_Segmentation_LA.png|200px]]<br />
| |<br />
<br />
== [[Projects:SegmentationEndocardialWall|Segmentation of the Endocardial Wall of the Left Atrium]] ==<br />
<br />
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]<br />
<br />
<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.<br />
<br />
|-<br />
<br />
| | [[Image:Pain1.JPG|200px]]<br />
| |<br />
<br />
== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==<br />
<br />
Pain assessment in patients who are unable to verbally<br />
communicate with medical staff is a challenging problem<br />
in patient critical care. The fundamental limitations in sedation<br />
and pain assessment in the intensive care unit (ICU) stem<br />
from subjective assessment criteria, rather than quantifiable,<br />
measurable data for ICU sedation and analgesia. This often<br />
results in poor quality and inconsistent treatment of patient<br />
agitation and pain from nurse to nurse. Recent advancements in<br />
pattern recognition techniques using a relevance vector machine<br />
algorithm can assist medical staff in assessing sedation and pain<br />
by constantly monitoring the patient and providing the clinician<br />
with quantifiable data for ICU sedation. In this paper, we show<br />
that the pain intensity assessment given by a computer classifier<br />
has a strong correlation with the pain intensity assessed by<br />
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]<br />
<br />
<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).<br />
<br />
|-<br />
<br />
| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==<br />
<br />
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.<br />
<br />
|-<br />
<br />
| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]<br />
| |<br />
<br />
== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==<br />
<br />
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]<br />
<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.<br />
<br />
|-<br />
<br />
| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==<br />
<br />
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]<br />
<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in preparation)<br />
<br />
<br />
<br />
|-<br />
<br />
| | [[Image:RadOnc HN seg.png|200px]]<br />
| |<br />
<br />
== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==<br />
<br />
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]<br />
<br />
<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).<br />
<br />
<br />
<br />
|-<br />
<br />
<br />
| | [[Image:Circle seg.PNG|200px|]]<br />
| |<br />
<br />
== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==<br />
<br />
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99<br />
<br />
|-<br />
| | [[Image:ZoomedResultWithModel.png|200px]]<br />
| |<br />
<br />
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==<br />
<br />
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.<br />
<br />
|-<br />
<br />
| | [[Image:P1_small.png|200px|]]<br />
| |<br />
<br />
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==<br />
<br />
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.<br />
<br />
|-<br />
<br />
| | [[Image:BasePair3DModel.JPG|200px|]]<br />
| |<br />
<br />
== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==<br />
<br />
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]<br />
<br />
<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.<br />
<br />
|-<br />
<br />
| | [[Image:TruckInitialization.png|200px|]]<br />
| |<br />
<br />
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==<br />
<br />
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by<br />
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]<br />
<br />
<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.<br />
<br />
|-<br />
<br />
| | [[Image:ShapeBasePstSegSlicer.png|200px|]]<br />
| |<br />
<br />
== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==<br />
<br />
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.<br />
<br />
<br />
|-<br />
<br />
| | [[Image:MultiObjSeg.png|200px|]]<br />
| |<br />
<br />
== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==<br />
<br />
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]<br />
| |<br />
<br />
== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==<br />
<br />
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]<br />
<br />
<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).<br />
<br />
|-<br />
<br />
| | [[Image:Results brain sag.JPG|200px]]<br />
| |<br />
<br />
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==<br />
<br />
The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]<br />
<br />
<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.<br />
<br />
<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.<br />
<br />
|-<br />
<br />
| | [[Image:Gatech caudateBands.PNG|200px]]<br />
| |<br />
<br />
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==<br />
<br />
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]<br />
<br />
|-<br />
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| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]<br />
| |<br />
<br />
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==<br />
<br />
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Basis membership.png|200px]]<br />
| |<br />
<br />
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==<br />
<br />
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]<br />
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|-<br />
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| | [[Image:Dlpfc1.jpg|200px|]]<br />
| |<br />
<br />
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]<br />
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|-<br />
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| | [[Image:Striatum1.png|200px|]]<br />
| |<br />
<br />
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Brain-flat.PNG|200px]]<br />
| |<br />
<br />
== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==<br />
<br />
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]<br />
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|-<br />
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| | [[Image:Fig1yan.PNG|200px|]]<br />
| |<br />
<br />
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==<br />
<br />
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.<br />
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|-<br />
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| | [[Image:Fig67.png|200px|]]<br />
| |<br />
<br />
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==<br />
<br />
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]<br />
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|-<br />
<br />
| | [[Image:Stochastic-snake.png|200px|]]<br />
| |<br />
<br />
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==<br />
<br />
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]<br />
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|-<br />
<br />
| | [[Image:GT-SulciOutlining1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==<br />
<br />
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]<br />
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|-<br />
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| | [[Image:Table1.png|200px|]]<br />
| |<br />
<br />
== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==<br />
<br />
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]<br />
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|-<br />
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| | [[Image:Gatech SlicerModel2.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==<br />
<br />
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]<br />
<br />
|}</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Algorithm:GATech&diff=48473Algorithm:GATech2010-02-08T20:43:01Z<p>Vandymohan: /* Group Study on DW-MRI using the Tubular Surface Model */</p>
<hr />
<div> Back to [[Algorithm:Main|NA-MIC Algorithms]]<br />
__NOTOC__<br />
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =<br />
<br />
At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.<br />
<br />
= Georgia Tech Projects =<br />
<br />
<br />
<br />
{| cellpadding="10" style="text-align:left;"<br />
<br />
| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]<br />
| style="width:85%" |<br />
<br />
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==<br />
<br />
The ability to detect and measure non-calcified plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]<br />
<br />
<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.<br />
|-<br />
<br />
| | [[Image:3D_Segmentation_LA.png|200px]]<br />
| |<br />
<br />
== [[Projects:SegmentationEndocardialWall|Segmentation of the Endocardial Wall of the Left Atrium]] ==<br />
<br />
Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]<br />
<br />
<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.<br />
<br />
|-<br />
<br />
| | [[Image:Pain1.JPG|200px]]<br />
| |<br />
<br />
== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==<br />
<br />
Pain assessment in patients who are unable to verbally<br />
communicate with medical staff is a challenging problem<br />
in patient critical care. The fundamental limitations in sedation<br />
and pain assessment in the intensive care unit (ICU) stem<br />
from subjective assessment criteria, rather than quantifiable,<br />
measurable data for ICU sedation and analgesia. This often<br />
results in poor quality and inconsistent treatment of patient<br />
agitation and pain from nurse to nurse. Recent advancements in<br />
pattern recognition techniques using a relevance vector machine<br />
algorithm can assist medical staff in assessing sedation and pain<br />
by constantly monitoring the patient and providing the clinician<br />
with quantifiable data for ICU sedation. In this paper, we show<br />
that the pain intensity assessment given by a computer classifier<br />
has a strong correlation with the pain intensity assessed by<br />
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]<br />
<br />
<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).<br />
<br />
|-<br />
<br />
| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==<br />
<br />
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.<br />
<br />
|-<br />
<br />
| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]<br />
| |<br />
<br />
== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==<br />
<br />
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]<br />
<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.<br />
<br />
|-<br />
<br />
| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==<br />
<br />
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]<br />
<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in preparation)<br />
<br />
<br />
<br />
|-<br />
<br />
| | [[Image:RadOnc HN seg.png|200px]]<br />
| |<br />
<br />
== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==<br />
<br />
We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]<br />
<br />
<font color="red">'''New: '''</font> I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).<br />
<br />
<br />
<br />
|-<br />
<br />
<br />
| | [[Image:Circle seg.PNG|200px|]]<br />
| |<br />
<br />
== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==<br />
<br />
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99<br />
<br />
|-<br />
| | [[Image:ZoomedResultWithModel.png|200px]]<br />
| |<br />
<br />
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==<br />
<br />
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.<br />
<br />
|-<br />
<br />
| | [[Image:P1_small.png|200px|]]<br />
| |<br />
<br />
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==<br />
<br />
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.<br />
<br />
|-<br />
<br />
| | [[Image:BasePair3DModel.JPG|200px|]]<br />
| |<br />
<br />
== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==<br />
<br />
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]<br />
<br />
<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.<br />
<br />
|-<br />
<br />
| | [[Image:TruckInitialization.png|200px|]]<br />
| |<br />
<br />
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==<br />
<br />
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by<br />
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]<br />
<br />
<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.<br />
<br />
|-<br />
<br />
| | [[Image:ShapeBasePstSegSlicer.png|200px|]]<br />
| |<br />
<br />
== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==<br />
<br />
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.<br />
<br />
<br />
|-<br />
<br />
| | [[Image:MultiObjSeg.png|200px|]]<br />
| |<br />
<br />
== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==<br />
<br />
Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]<br />
| |<br />
<br />
== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==<br />
<br />
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]<br />
<br />
<font color="red">'''New: '''</font> Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).<br />
<br />
|-<br />
<br />
| | [[Image:Results brain sag.JPG|200px]]<br />
| |<br />
<br />
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==<br />
<br />
The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]<br />
<br />
<font color="red">'''New: '''</font> Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.<br />
<br />
<font color="red">'''New: '''</font> Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.<br />
<br />
|-<br />
<br />
| | [[Image:Gatech caudateBands.PNG|200px]]<br />
| |<br />
<br />
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==<br />
<br />
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]<br />
| |<br />
<br />
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==<br />
<br />
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Basis membership.png|200px]]<br />
| |<br />
<br />
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==<br />
<br />
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Dlpfc1.jpg|200px|]]<br />
| |<br />
<br />
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Striatum1.png|200px|]]<br />
| |<br />
<br />
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Brain-flat.PNG|200px]]<br />
| |<br />
<br />
== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==<br />
<br />
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Fig1yan.PNG|200px|]]<br />
| |<br />
<br />
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==<br />
<br />
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.<br />
<br />
|-<br />
<br />
| | [[Image:Fig67.png|200px|]]<br />
| |<br />
<br />
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==<br />
<br />
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Stochastic-snake.png|200px|]]<br />
| |<br />
<br />
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==<br />
<br />
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:GT-SulciOutlining1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==<br />
<br />
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Table1.png|200px|]]<br />
| |<br />
<br />
== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==<br />
<br />
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Gatech SlicerModel2.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==<br />
<br />
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]<br />
<br />
|}</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2010_Winter_Project_Week_TubularTreeSeg&diff=475822010 Winter Project Week TubularTreeSeg2010-01-08T16:34:24Z<p>Vandymohan: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br />
Image:MohanGT-TubSegSlicerModule-IntegrationResult1.png|Snapshot of Tubular Surface Segmentation framework as a Slicer module, and obtained result<br />
</gallery><br />
<br />
<br />
==Key Investigators==<br />
* Vandana Mohan, Allen Tannenbaum (Georgia Tech)<br />
* Marek Kubicki (BWH)<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objective</h3><br />
Implement a Slicer module for the Tubular Tree segmentation framework of Mohan et al.<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
Implement as a command-line module via integration of current MATLAB implementation.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
* The algorithm is available in MATLAB with parts of the framework as C code (via mex). <br />
* During winter project week 2010: Framework integrated into Slicer as MATLAB module. Tested for CB segmentation. (See above image for snapshot of module view and result from Slicer.)<br />
* Next steps: Enable branching detection in framework, and test on vessel segmentation.<br />
<br />
</div><br />
</div><br />
<br />
<div style="width: 97%; float: left;"><br />
<br />
==References==<br />
"Tubular Surface Segmentation for extracting anatomical structures from medical imagery"; Vandana Mohan, Ganesh Sundaramoorthi and Allen Tannenbaum; IEEE Transactions in Medical Imaging (in review)<br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2010_Winter_Project_Week_TubularTreeSeg&diff=475682010 Winter Project Week TubularTreeSeg2010-01-08T16:18:11Z<p>Vandymohan: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br />
Image:MohanGT-TubSegSlicerModule-IntegrationResult1.png|Snapshot of Tubular Surface Segmentation framework as a Slicer module, and obtained result<br />
</gallery><br />
<br />
<br />
==Key Investigators==<br />
* Vandana Mohan (Georgia Tech)<br />
* Marek Kubicki (BWH)<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objective</h3><br />
Implement a Slicer module for the Tubular Tree segmentation framework of Mohan et al.<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
Implement as a command-line module via integration of current MATLAB implementation.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
* The algorithm is available in MATLAB with parts of the framework as C code (via mex). <br />
* During winter project week 2010: Framework integrated into Slicer as MATLAB module. Tested for CB segmentation. (See above image for snapshot of module view and result from Slicer.)<br />
* Next steps: Enable branching detection in framework, and test on vessel segmentation.<br />
<br />
</div><br />
</div><br />
<br />
<div style="width: 97%; float: left;"><br />
<br />
==References==<br />
"Tubular Surface Segmentation for extracting anatomical structures from medical imagery"; Vandana Mohan, Ganesh Sundaramoorthi and Allen Tannenbaum; IEEE Transactions in Medical Imaging (in review)<br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2010_Winter_Project_Week_TubularTreeSeg&diff=475552010 Winter Project Week TubularTreeSeg2010-01-08T16:02:47Z<p>Vandymohan: /* Key Investigators */</p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br />
Image:MohanGT-TubSegSlicerModule-IntegrationResult1.png|CB Segmentation result from Tubular Surface Segmentation framework (integrated into Slicer)<br />
</gallery><br />
<br />
<br />
==Key Investigators==<br />
* Vandana Mohan (Georgia Tech)<br />
* Marek Kubicki (BWH)<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objective</h3><br />
Implement a Slicer module for the Tubular Tree segmentation framework of Mohan et al.<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
Implement as a command-line module via integration of current MATLAB implementation.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
* The algorithm is available in MATLAB with parts of the framework as C code (via mex). <br />
* During winter project week 2010: Framework integrated into Slicer as MATLAB module. Tested for CB segmentation. (See above image for snapshot of module view and result from Slicer.)<br />
* Next steps: Enable branching detection in framework, and test on vessel segmentation.<br />
<br />
</div><br />
</div><br />
<br />
<div style="width: 97%; float: left;"><br />
<br />
==References==<br />
"Tubular Surface Segmentation for extracting anatomical structures from medical imagery"; Vandana Mohan, Ganesh Sundaramoorthi and Allen Tannenbaum; IEEE Transactions in Medical Imaging (in review)<br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2010_Winter_Project_Week_TubularTreeSeg&diff=475202010 Winter Project Week TubularTreeSeg2010-01-08T07:27:00Z<p>Vandymohan: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br />
Image:MohanGT-TubSegSlicerModule-IntegrationResult1.png|CB Segmentation result from Tubular Surface Segmentation framework (integrated into Slicer)<br />
</gallery><br />
<br />
<br />
==Key Investigators==<br />
* Vandana Mohan (Georgia Tech)<br />
* Marek Kubicki (BWH)<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objective</h3><br />
Implement a Slicer module for the Tubular Tree segmentation framework of Mohan et al.<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
Implement as a command-line module via integration of current MATLAB implementation.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
The algorithm is available in MATLAB with parts of the framework as C code (via mex). <br />
<br />
</div><br />
</div><br />
<br />
<div style="width: 97%; float: left;"><br />
<br />
==References==<br />
"Tubular Surface Segmentation for extracting anatomical structures from medical imagery"; Vandana Mohan, Ganesh Sundaramoorthi and Allen Tannenbaum; IEEE Transactions in Medical Imaging (in review)<br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=File:MohanGT-TubSegSlicerModule-IntegrationResult1.png&diff=47519File:MohanGT-TubSegSlicerModule-IntegrationResult1.png2010-01-08T07:24:56Z<p>Vandymohan: Preliminary view of a run of the Tubular Surface Segmentation framework integrated into Slicer as a MATLAB module.</p>
<hr />
<div>Preliminary view of a run of the Tubular Surface Segmentation framework integrated into Slicer as a MATLAB module.</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2010_Winter_Project_Week_TubularTreeSeg&diff=457422010 Winter Project Week TubularTreeSeg2009-12-04T18:03:20Z<p>Vandymohan: /* References */</p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br />
</gallery><br />
<br />
<br />
==Key Investigators==<br />
* Vandana Mohan (Georgia Tech)<br />
* Marek Kubicki (BWH)<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objective</h3><br />
Implement a Slicer module for the Tubular Tree segmentation framework of Mohan et al.<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
Implement as a command-line module via integration of current MATLAB implementation.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
The algorithm is available in MATLAB with parts of the framework as C code (via mex). <br />
<br />
</div><br />
</div><br />
<br />
<div style="width: 97%; float: left;"><br />
<br />
==References==<br />
"Tubular Surface Segmentation for extracting anatomical structures from medical imagery"; Vandana Mohan, Ganesh Sundaramoorthi and Allen Tannenbaum; IEEE Transactions in Medical Imaging (in review)<br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2010_Winter_Project_Week_TubularTreeSeg&diff=457412010 Winter Project Week TubularTreeSeg2009-12-04T18:02:52Z<p>Vandymohan: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br />
</gallery><br />
<br />
<br />
==Key Investigators==<br />
* Vandana Mohan (Georgia Tech)<br />
* Marek Kubicki (BWH)<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objective</h3><br />
Implement a Slicer module for the Tubular Tree segmentation framework of Mohan et al.<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
Implement as a command-line module via integration of current MATLAB implementation.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
The algorithm is available in MATLAB with parts of the framework as C code (via mex). <br />
<br />
</div><br />
</div><br />
<br />
<div style="width: 97%; float: left;"><br />
<br />
==References==<br />
Tubular Surface Segmentation for extracting anatomical structures from medical imagery; Vandana Mohan, Ganesh Sundaramoorthi and Allen Tannenbaum; IEEE Transactions in Medical Imaging (in review)<br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2010_Winter_Project_Week_TubularTreeSeg&diff=457402010 Winter Project Week TubularTreeSeg2009-12-04T18:02:20Z<p>Vandymohan: Created page with '__NOTOC__ <gallery> Image:PW-SLC2010.png|Projects List </gallery> ==Key Investigators== * Andras Lasso (Queen's University) * Yi Gao (Geor…'</p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]<br />
</gallery><br />
<br />
<br />
==Key Investigators==<br />
* Andras Lasso (Queen's University)<br />
* Yi Gao (Georgia Tech)<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objective</h3><br />
Implement a Slicer module for the Tubular Tree segmentation framework of Mohan et al.<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
Implement as a command-line module via integration of current MATLAB implementation.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
The algorithm is available in MATLAB with parts of the framework as C code (via mex). <br />
<br />
</div><br />
</div><br />
<br />
<div style="width: 97%; float: left;"><br />
<br />
==References==<br />
Tubular Surface Segmentation for extracting anatomical structures from medical imagery; Vandana Mohan, Ganesh Sundaramoorthi and Allen Tannenbaum; IEEE Transactions in Medical Imaging (in review)<br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2010_Winter_Project_Week&diff=457392010 Winter Project Week2009-12-04T17:57:53Z<p>Vandymohan: /* Segmentation */</p>
<hr />
<div> Back to [[Project Events]], [[AHM_2010]], [[Events]]<br />
<br />
__NOTOC__<br />
<br />
==Background==<br />
<br />
From January 4-8, 2010, the tenth project week for hands-on research and development activity in Image-Guided Therapy and Neuroscience applications will be hosted in Salt Lake City, Utah. Participant engange 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. <br />
<br />
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. <br />
<br />
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.<br />
<br />
A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].<br />
<br />
== Dates.Venue.Registration ==<br />
<br />
Please [[AHM_2010#Dates._Venue._Registration| click here for Dates, Venue, and Registration]] for this event.<br />
<br />
== Agenda==<br />
<br />
Please [[AHM_2010#Agenda|click here for the agenda for AHM 2010 and Project Week]].<br />
<br />
== Modules and extensions==<br />
<br />
<br />
* [[Media:3DSlicer-Modules%2BExtensions-2009-11-27.ppt|Overview]]<br />
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation-3.5#Requirements_for_Modules Requirements for modules]<br />
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation-3.5#Introduction User-side explanations]<br />
* [http://wiki.slicer.org/slicerWiki/index.php/Slicer3:Extensions Developer-side explanations]<br />
<br />
==Projects==<br />
<br />
=== Segmentation ===<br />
#[[2010_Winter_Project_Week_The_Vascular_Modeling_Toolkit_in_3D_Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Steve Pieper)<br />
#[[2010_Winter_Project_Week_RobustStatisticsDrivenActiveContourSegmentation|Active contour segmentation using robust statistics]] (Yi Gao, Allen Tannenbaum, GT; Andriy Fedorov, Katie Hayes Ron Kikinis BWH)<br />
#[[2010_Winter_Project_Week_SegmentationWizard|High Level Wizard for Segmentation of Images]] (Mark Scully, Jeremy Bockholt, Steve Pieper)<br />
#[[2010_Winter_Project_Week_LongitudinalLupusAnalyses|Longitudinal Analyses of Lesions in Lupus]] (Mark Scully, Jeremy Bockholt, Steve Pieper)<br />
#[[2010_Winter_Project_Week_MultiscaleLupusAnalyses|Multiscale Analyses of Lupus Patients]] (Mark Scully, Jeremy Bockholt, Steve Pieper)<br />
#[[2010_Winter_Project_Week_ProstateSeg|Prostate segmentation using shape-based method]] (Andras Lasso, Yi Gao)<br />
#[[2010_Winter_Project_Week_TubularTreeSeg|Tubular Tree Segmentation for brain and cardiac imagery]] (Vandana Mohan, Allen Tannenbaum, GT; Marek Kubicki, BWH)<br />
<br />
=== Registration ===<br />
#[[2010_Winter_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library Project]] (Dominik Meier, Casey Goodlett, Ron Kikinis)<br />
#[[Tissue_Dependent_Registration|Registration with Varying Elastic Parameters]] (Petter Risholm, Sandy Wells)<br />
<br />
=== IGT ===<br />
# [[2010_Winter_Project_Week_Fast_Imaging_Library_%2B_Siemens_EPI]] (Scott Hoge, Nick Todd, Dennis Parker, Katie Hayes)<br />
# [[2010_Winter_Project_Week_MRI_Guided_Robotic_Prostate_Intervention| MRI-guided Robotic Prostate Intervention]] (Andras Lasso and Junichi Tokuda)<br />
<br />
=== Radiotherapy ===<br />
# [[2010_Winter_Project_Week_DicomRT_Plugin]] (Greg Sharp, others)<br />
<br />
=== Analysis ===<br />
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=== Informatics ===<br />
#XNAT Desktop User Interface (Dan M, Wendy P, Ron K)<br />
#Slicer 3 XNAT Performance Tuning (Wendy P, Dan M, Tim Olson, Nicole Aucoin)<br />
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=== Diffusion ===<br />
#[[ 2010_Winter_Project_Week_HARDI_RSH|Integration of Real Spherical Harmonic basis for HARDI models]] (Luke Bloy, C-F Westin)<br />
#[[ 2010_Winter_Project_Week_Tractography|Filtered tractography]] (James Malcolm, Peter Savadjiev, Yogesh Rathi, C-F Westin, Casey Goodlett)<br />
#[[ 2010_Winter_Project_Week_HARDI_CONNECTIVITY|Connectivity Study of Neonatal Brain Data using HARDI Techniques]] ( Yundi(Wendy) Shi, Deepika Mahalingam, Martin Styner )<br />
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=== Python ===<br />
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=== NA-MIC Kit Internals ===<br />
#Testing for Extensions (Steve, Andre, Jim, Julien Jomier, Katie Hayes, Stuart Wallace)<br />
#[[2010_Winter_Project_Week_SPECTRE_3DSlicer_Integration|Integration of SPECTRE Java module into 3D Slicer]] (Nicole Aucoin, Aaron Carass, Min Chen)<br />
#VTK 3D Widgets in Slicer3 (Nicole Aucoin, Karthik, Will)<br />
#Updates to Slicer3 Colors Module (Nicole Aucoin)<br />
#Whole Body CT/MR open source data set publishing (Dan Marcus)<br />
#CMAKE Build process (Dave Partyka, Katie Hayes)<br />
#Integration of XNAT Packaging for Slicer Internals (Dan, Tim Olsen, Dave Partyka, Wendy, Randy)<br />
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=== Execution Model ===<br />
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=== Preparation ===<br />
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# 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]<br />
# Starting Thursday, October 15th, 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:<br />
#*October 15: Engineering Infrastructure Projects<br />
#*October 22: Funded External Collaboration Projects<br />
#*October 29: Funded External Collaboration Projects<br />
#*November 5: DPB Projects <br />
#*November 19: DPB Projects <br />
#*December 3: Other/new collaborations<br />
#*December 10: Finalize Engineering Projects<br />
#*December 17: Loose Ends<br />
#By December 17, 2010: [[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.<br />
# By December 17, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)<br />
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]<br />
## 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)<br />
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)<br />
## 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)<br />
# 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...</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Algorithm:GATech&diff=43171Algorithm:GATech2009-09-30T01:32:59Z<p>Vandymohan: </p>
<hr />
<div> Back to [[Algorithm:Main|NA-MIC Algorithms]]<br />
__NOTOC__<br />
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =<br />
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At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.<br />
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= Georgia Tech Projects =<br />
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{| cellpadding="10" style="text-align:left;"<br />
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== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==<br />
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The ability to detect and measure non-calcified plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]<br />
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<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.<br />
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== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==<br />
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This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]<br />
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<font color="red">'''New: '''</font> Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.<br />
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==<br />
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We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]<br />
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<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.<br />
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==<br />
<br />
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]<br />
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<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).<br />
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|-<br />
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| | [[Image:Circle seg.PNG|200px|]]<br />
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== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==<br />
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Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]<br />
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<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99<br />
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==<br />
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In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]<br />
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<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.<br />
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==<br />
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Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]<br />
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<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.<br />
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== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==<br />
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High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]<br />
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<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.<br />
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==<br />
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In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by<br />
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]<br />
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<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.<br />
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== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==<br />
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The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]<br />
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== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==<br />
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3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]<br />
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==<br />
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The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]<br />
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== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==<br />
<br />
Pain assessment in patients who are unable to verbally<br />
communicate with medical staff is a challenging problem<br />
in patient critical care. The fundamental limitations in sedation<br />
and pain assessment in the intensive care unit (ICU) stem<br />
from subjective assessment criteria, rather than quantifiable,<br />
measurable data for ICU sedation and analgesia. This often<br />
results in poor quality and inconsistent treatment of patient<br />
agitation and pain from nurse to nurse. Recent advancements in<br />
pattern recognition techniques using a relevance vector machine<br />
algorithm can assist medical staff in assessing sedation and pain<br />
by constantly monitoring the patient and providing the clinician<br />
with quantifiable data for ICU sedation. In this paper, we show<br />
that the pain intensity assessment given by a computer classifier<br />
has a strong correlation with the pain intensity assessed by<br />
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]<br />
<br />
<font color="red">'''New: '''</font> B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (in submission).<br />
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== [[Projects:SegmentationEpicardialWall|Segmentation of the Epicardial Wall of the Left Atrium]] ==<br />
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Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation.<br />
Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]<br />
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<font color="red">'''New: '''</font> Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Epicardial Wall of the Left Atrium Using<br />
Statistical Shape Learning and Local Curve Statistics, SPIE Medical Imaging 2010 (in submission).<br />
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== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==<br />
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To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]<br />
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== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==<br />
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Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]<br />
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==<br />
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We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]<br />
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== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==<br />
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In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]<br />
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== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]<br />
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== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==<br />
<br />
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]<br />
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== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==<br />
<br />
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]<br />
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<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.<br />
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==<br />
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This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]<br />
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==<br />
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New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]<br />
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==<br />
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We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]<br />
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== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==<br />
<br />
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]<br />
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==<br />
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This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]<br />
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|}</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentationPopStudy&diff=43170Projects:TubularSurfaceSegmentationPopStudy2009-09-30T01:21:38Z<p>Vandymohan: </p>
<hr />
<div> Back to [[Algorithm:GATech|Georgia Tech Algorithms]]<br />
__NOTOC__<br />
= Group study using the Tubular Surface model =<br />
<br />
= Description =<br />
<br />
We have proposed a new framework for performing group studies on DW-MRI data using the Tubular Surface Model to study white-matter properties. We show that the model facilitates population studies by the natural registration that occurs by the sampling of WM properties along the fiber bundles center-lines. Further, by allowing us to characterize the discrimination ability of local regions of the fiber bundles, the framework allows us to identify the regions that are "affected" by the disorder under study. In our experiments, we have applied the framework to study the Cingulum Bundle towards discriminating Schizophrenia.<br />
<br />
<br />
= Key Investigators =<br />
<br />
Georgia Tech: Allen Tannenbaum, Vandana Mohan<br />
<br />
BWH: Marek Kubicki<br />
<br />
= Publications =<br />
''In Print''<br />
* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADWIReorientation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]<br />
<br />
== Some Results<br />
The results below show the visualization of t-statistics with respect to the extracted features, with discrimination ability increasing from green to red. This demonstrates the ability of the framework to visualize the role of different regions of the fiber bundle in Schizophrenia.<br />
* [[Image:GT-PopStudyVis_OnCBs_Case19-View1.jpg | Visualization of T-statistics on Cingulum Bundle surface (View 1)| 300px]] Visualization of T-statistics on Cingulum Bundle surface (View 1)<br />
* [[Image:GT-PopStudyVis_OnCBs_Case19-View2.jpg | Visualization of T-statistics on Cingulum Bundle surface (View 2)| 300px]] Visualization of T-statistics on Cingulum Bundle surface (View 2)<br />
<br />
[[Category: MRI]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=File:GT-PopStudyVis_OnCBs_Case19-View2.jpg&diff=43169File:GT-PopStudyVis OnCBs Case19-View2.jpg2009-09-30T01:19:02Z<p>Vandymohan: </p>
<hr />
<div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=File:GT-PopStudyVis_OnCBs_Case19-View1.jpg&diff=43168File:GT-PopStudyVis OnCBs Case19-View1.jpg2009-09-30T01:18:18Z<p>Vandymohan: </p>
<hr />
<div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentationPopStudy&diff=43167Projects:TubularSurfaceSegmentationPopStudy2009-09-30T01:09:33Z<p>Vandymohan: </p>
<hr />
<div> Back to [[Algorithm:GATech|Georgia Tech Algorithms]]<br />
__NOTOC__<br />
= Group study using the Tubular Surface model =<br />
<br />
= Description =<br />
<br />
We have proposed a new framework for performing group studies on DW-MRI data using the Tubular Surface Model to study white-matter properties. We show that the model facilitates population studies by the natural registration that occurs by the sampling of WM properties along the fiber bundles center-lines. Further, by allowing us to characterize the discrimination ability of local regions of the fiber bundles, the framework allows us to identify the regions that are "affected" by the disorder under study. In our experiments, we have applied the framework to study the Cingulum Bundle towards discriminating Schizophrenia.<br />
<br />
<br />
= Key Investigators =<br />
<br />
Georgia Tech: Allen Tannenbaum, Vandana Mohan<br />
BWH: Marek Kubicki<br />
<br />
= Publications =<br />
''In Print''<br />
* [http://www.na-mic.org/publications/pages/display?search=Projects%3ADWIReorientation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]<br />
<br />
[[Category: MRI]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentationPopStudy&diff=43143Projects:TubularSurfaceSegmentationPopStudy2009-09-29T22:20:18Z<p>Vandymohan: </p>
<hr />
<div> Back to [[Algorithm:GATech|Georgia Tech Algorithms]]<br />
__NOTOC__<br />
<br />
= Group Study using the Tubular Surface Model =<br />
We have proposed a new framework for performing group studies on DW-MRI data using the Tubular Surface Model to study white-matter properties. We show that the model facilitates population studies by the natural registration that occurs by the sampling of WM properties along the fiber bundles center-lines. Further, by allowing us to characterize the discrimination ability of local regions of the fiber bundles, the framework allows us to identify the regions that are "affected" by the disorder under study. In our experiments, we have applied the framework to study the Cingulum Bundle towards discriminating Schizophrenia.</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentationPopStudy&diff=43142Projects:TubularSurfaceSegmentationPopStudy2009-09-29T22:19:28Z<p>Vandymohan: Created page with 'We have proposed a new framework for performing group studies on DW-MRI data using the Tubular Surface Model to study white-matter properties. We show that the model facilitates …'</p>
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<div>We have proposed a new framework for performing group studies on DW-MRI data using the Tubular Surface Model to study white-matter properties. We show that the model facilitates population studies by the natural registration that occurs by the sampling of WM properties along the fiber bundles center-lines. Further, by allowing us to characterize the discrimination ability of local regions of the fiber bundles, the framework allows us to identify the regions that are "affected" by the disorder under study. In our experiments, we have applied the framework to study the Cingulum Bundle towards discriminating Schizophrenia.</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Algorithm:GATech&diff=43138Algorithm:GATech2009-09-29T21:52:16Z<p>Vandymohan: </p>
<hr />
<div> Back to [[Algorithm:Main|NA-MIC Algorithms]]<br />
__NOTOC__<br />
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =<br />
<br />
At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.<br />
<br />
= Georgia Tech Projects =<br />
<br />
{| cellpadding="10" style="text-align:left;"<br />
<br />
| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]<br />
| style="width:85%" |<br />
<br />
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==<br />
<br />
The ability to detect and measure non-calcified plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]<br />
<br />
<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.<br />
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|-<br />
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| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==<br />
<br />
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.<br />
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|-<br />
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]<br />
| |<br />
<br />
== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==<br />
<br />
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]<br />
<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.<br />
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|-<br />
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]<br />
| |<br />
<br />
== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==<br />
<br />
We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]<br />
<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).<br />
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|-<br />
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| | [[Image:Circle seg.PNG|200px|]]<br />
| |<br />
<br />
== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==<br />
<br />
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99<br />
<br />
|-<br />
| | [[Image:ZoomedResultWithModel.png|200px]]<br />
| |<br />
<br />
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==<br />
<br />
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.<br />
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|-<br />
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| | [[Image:P1_small.png|200px|]]<br />
| |<br />
<br />
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==<br />
<br />
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.<br />
<br />
|-<br />
<br />
| | [[Image:BasePair3DModel.JPG|200px|]]<br />
| |<br />
<br />
== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==<br />
<br />
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]<br />
<br />
<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.<br />
<br />
|-<br />
<br />
| | [[Image:TruckInitialization.png|200px|]]<br />
| |<br />
<br />
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==<br />
<br />
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by<br />
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]<br />
<br />
<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.<br />
<br />
|-<br />
<br />
| | [[Image:ShapeBasePstSegSlicer.png|200px|]]<br />
| |<br />
<br />
== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==<br />
<br />
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]<br />
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|-<br />
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| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]<br />
| |<br />
<br />
== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==<br />
<br />
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]<br />
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|-<br />
<br />
| | [[Image:Results brain sag.JPG|200px]]<br />
| |<br />
<br />
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==<br />
<br />
The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]<br />
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|-<br />
<br />
| | [[Image:Gatech caudateBands.PNG|200px]]<br />
| |<br />
<br />
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==<br />
<br />
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]<br />
| |<br />
<br />
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==<br />
<br />
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Basis membership.png|200px]]<br />
| |<br />
<br />
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==<br />
<br />
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Dlpfc1.jpg|200px|]]<br />
| |<br />
<br />
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Striatum1.png|200px|]]<br />
| |<br />
<br />
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Brain-flat.PNG|200px]]<br />
| |<br />
<br />
== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==<br />
<br />
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]<br />
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|-<br />
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| | [[Image:Fig1yan.PNG|200px|]]<br />
| |<br />
<br />
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==<br />
<br />
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.<br />
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|-<br />
<br />
| | [[Image:Fig67.png|200px|]]<br />
| |<br />
<br />
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==<br />
<br />
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]<br />
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|-<br />
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| | [[Image:Stochastic-snake.png|200px|]]<br />
| |<br />
<br />
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==<br />
<br />
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]<br />
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|-<br />
<br />
| | [[Image:GT-SulciOutlining1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==<br />
<br />
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]<br />
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|-<br />
<br />
| | [[Image:Table1.png|200px|]]<br />
| |<br />
<br />
== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==<br />
<br />
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]<br />
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|-<br />
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| | [[Image:Gatech SlicerModel2.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==<br />
<br />
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]<br />
<br />
|}</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Algorithm:GATech&diff=43134Algorithm:GATech2009-09-29T21:38:57Z<p>Vandymohan: </p>
<hr />
<div> Back to [[Algorithm:Main|NA-MIC Algorithms]]<br />
__NOTOC__<br />
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =<br />
<br />
At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.<br />
<br />
= Georgia Tech Projects =<br />
<br />
{| cellpadding="10" style="text-align:left;"<br />
<br />
| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]<br />
| style="width:85%" |<br />
<br />
== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==<br />
<br />
The ability to detect and measure non-calcified plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]<br />
<br />
<font color="red">'''New: '''</font> Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.<br />
<br />
|-<br />
<br />
| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==<br />
<br />
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.<br />
<br />
|-<br />
<br />
| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]<br />
| |<br />
<br />
== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==<br />
<br />
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]<br />
<br />
<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.<br />
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.<br />
<br />
|-<br />
<br />
| | [[Image:Circle seg.PNG|200px|]]<br />
| |<br />
<br />
== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==<br />
<br />
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99<br />
<br />
|-<br />
| | [[Image:ZoomedResultWithModel.png|200px]]<br />
| |<br />
<br />
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==<br />
<br />
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.<br />
<br />
|-<br />
<br />
| | [[Image:P1_small.png|200px|]]<br />
| |<br />
<br />
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==<br />
<br />
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.<br />
<br />
|-<br />
<br />
| | [[Image:BasePair3DModel.JPG|200px|]]<br />
| |<br />
<br />
== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==<br />
<br />
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]<br />
<br />
<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.<br />
<br />
|-<br />
<br />
| | [[Image:TruckInitialization.png|200px|]]<br />
| |<br />
<br />
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==<br />
<br />
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by<br />
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]<br />
<br />
<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.<br />
<br />
|-<br />
<br />
| | [[Image:ShapeBasePstSegSlicer.png|200px|]]<br />
| |<br />
<br />
== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==<br />
<br />
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]<br />
| |<br />
<br />
== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==<br />
<br />
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Results brain sag.JPG|200px]]<br />
| |<br />
<br />
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==<br />
<br />
The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Gatech caudateBands.PNG|200px]]<br />
| |<br />
<br />
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==<br />
<br />
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]<br />
| |<br />
<br />
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==<br />
<br />
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Basis membership.png|200px]]<br />
| |<br />
<br />
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==<br />
<br />
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Dlpfc1.jpg|200px|]]<br />
| |<br />
<br />
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Striatum1.png|200px|]]<br />
| |<br />
<br />
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Brain-flat.PNG|200px]]<br />
| |<br />
<br />
== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==<br />
<br />
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]<br />
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|-<br />
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| | [[Image:Fig1yan.PNG|200px|]]<br />
| |<br />
<br />
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==<br />
<br />
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.<br />
<br />
|-<br />
<br />
| | [[Image:Fig67.png|200px|]]<br />
| |<br />
<br />
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==<br />
<br />
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Stochastic-snake.png|200px|]]<br />
| |<br />
<br />
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==<br />
<br />
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]<br />
<br />
|-<br />
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| | [[Image:GT-SulciOutlining1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==<br />
<br />
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]<br />
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|-<br />
<br />
| | [[Image:Table1.png|200px|]]<br />
| |<br />
<br />
== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==<br />
<br />
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Gatech SlicerModel2.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==<br />
<br />
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]<br />
<br />
|}</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2009_Summer_Project_Week_TubularSurfaceSeg&diff=399532009 Summer Project Week TubularSurfaceSeg2009-06-26T03:04:35Z<p>Vandymohan: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW2009-v3.png|[[2009_Summer_Project_Week#Projects|Projects List]]<br />
Image:GTTub_Case39_CBs.png <br />
</gallery><br />
<br />
<br />
==Key Investigators==<br />
* Georgia Tech: Vandana Mohan, Allen Tannenbaum<br />
* BWH: Marek Kubicki, Doug Terry<br />
<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objectives</h3><br />
* To integrate the Tubular Surface Segmentation into Slicer3, and test it on Cingulum Bundle.<br />
* To evaluate the model's performance in population studies.<br />
<br />
<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
* The tubular surface segmentation has been proposed to extract tubular anatomical structures from medical imagery.<br />
* We will be integrating the framework into Slicer3 and testing it on DWI data for Cingulum Bundle Segmentation.<br />
* We will be working with clinical collaborators to interpret the population study results using the model.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
* The framework has been implemented and tested successfully in MATLAB (with mexed C code).<br />
<br />
</div><br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2009_Summer_Project_Week_TubularSurfaceSeg&diff=399522009 Summer Project Week TubularSurfaceSeg2009-06-26T03:03:38Z<p>Vandymohan: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW2009-v3.png|[[2009_Summer_Project_Week#Projects|Projects List]]<br />
</gallery><br />
<br />
Image:GTTub_Case39_CBs.png | CB Segmentation Result<br />
<br />
==Key Investigators==<br />
* Georgia Tech: Vandana Mohan, Allen Tannenbaum<br />
* BWH: Marek Kubicki, Doug Terry<br />
<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objectives</h3><br />
* To integrate the Tubular Surface Segmentation into Slicer3, and test it on Cingulum Bundle.<br />
* To evaluate the model's performance in population studies.<br />
<br />
<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
* The tubular surface segmentation has been proposed to extract tubular anatomical structures from medical imagery.<br />
* We will be integrating the framework into Slicer3 and testing it on DWI data for Cingulum Bundle Segmentation.<br />
* We will be working with clinical collaborators to interpret the population study results using the model.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
* The framework has been implemented and tested successfully in MATLAB (with mexed C code).<br />
<br />
</div><br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=File:GTTub_Case39_CBs.png&diff=39951File:GTTub Case39 CBs.png2009-06-26T03:02:56Z<p>Vandymohan: </p>
<hr />
<div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2009_Summer_Project_Week_TubularSurfaceSeg&diff=399502009 Summer Project Week TubularSurfaceSeg2009-06-26T03:02:33Z<p>Vandymohan: </p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW2009-v3.png|[[2009_Summer_Project_Week#Projects|Projects List]]<br />
</gallery><br />
<br />
Image:GTTub_Case39_CBs.png<br />
<br />
==Key Investigators==<br />
* Georgia Tech: Vandana Mohan, Allen Tannenbaum<br />
* BWH: Marek Kubicki, Doug Terry<br />
<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objectives</h3><br />
* To integrate the Tubular Surface Segmentation into Slicer3, and test it on Cingulum Bundle.<br />
* To evaluate the model's performance in population studies.<br />
<br />
<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
* The tubular surface segmentation has been proposed to extract tubular anatomical structures from medical imagery.<br />
* We will be integrating the framework into Slicer3 and testing it on DWI data for Cingulum Bundle Segmentation.<br />
* We will be working with clinical collaborators to interpret the population study results using the model.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
* The framework has been implemented and tested successfully in MATLAB (with mexed C code).<br />
<br />
</div><br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2009_Summer_Project_Week_TubularSurfaceSeg&diff=394192009 Summer Project Week TubularSurfaceSeg2009-06-23T20:09:32Z<p>Vandymohan: /* Key Investigators */</p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW2009-v3.png|[[2009_Summer_Project_Week#Projects|Projects List]]<br />
</gallery><br />
<br />
<br />
==Key Investigators==<br />
* Georgia Tech: Vandana Mohan, Allen Tannenbaum<br />
* BWH: Marek Kubicki, Doug Terry<br />
<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objectives</h3><br />
* To integrate the Tubular Surface Segmentation into Slicer3, and test it on Cingulum Bundle.<br />
* To evaluate the model's performance in population studies.<br />
<br />
<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
* The tubular surface segmentation has been proposed to extract tubular anatomical structures from medical imagery.<br />
* We will be integrating the framework into Slicer3 and testing it on DWI data for Cingulum Bundle Segmentation.<br />
* We will be working with clinical collaborators to interpret the population study results using the model.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
* The framework has been implemented and tested successfully in MATLAB (with mexed C code).<br />
<br />
</div><br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2009_Summer_Project_Week_TubularSurfaceSeg&diff=394182009 Summer Project Week TubularSurfaceSeg2009-06-23T20:07:56Z<p>Vandymohan: /* Key Investigators */</p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW2009-v3.png|[[2009_Summer_Project_Week#Projects|Projects List]]<br />
</gallery><br />
<br />
<br />
==Key Investigators==<br />
* Georgia Tech: Vandana Mohan, Allen Tannenbaum<br />
* BWH: Marek Kubicki, Doug Terry<br />
<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objective</h3><br />
To integrate the Tubular Surface Segmentation into Slicer3, and test it on Cingulum Bundle.<br />
<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
* The tubular surface segmentation has been proposed to extract tubular anatomical structures from medical imagery.<br />
* We will be integrating the framework into Slicer3 and testing it on DWI data for Cingulum Bundle Segmentation.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
* The framework has been implemented and tested successfully in MATLAB (with mexed C code).<br />
<br />
</div><br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2009_Summer_Project_Week_TubularSurfaceSeg&diff=388622009 Summer Project Week TubularSurfaceSeg2009-06-17T18:02:21Z<p>Vandymohan: Created page with '__NOTOC__ <gallery> Image:PW2009-v3.png|Project Week Main Page </gallery> ==Key Investigators== * Vandana Mohan (Georgia Tech) * Allen Tannenbaum (...'</p>
<hr />
<div>__NOTOC__<br />
<gallery><br />
Image:PW2009-v3.png|[[2009_Summer_Project_Week|Project Week Main Page]]<br />
</gallery><br />
<br />
<br />
==Key Investigators==<br />
* Vandana Mohan (Georgia Tech)<br />
* Allen Tannenbaum (Georgia Tech)<br />
<br />
<div style="margin: 20px;"><br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Objective</h3><br />
To integrate the Tubular Surface Segmentation into Slicer3, and test it on CB Segmentation.<br />
<br />
<br />
</div><br />
<br />
<div style="width: 27%; float: left; padding-right: 3%;"><br />
<br />
<h3>Approach, Plan</h3><br />
<br />
* The tubular surface segmentation has been proposed to extract tubular anatomical structures from medical imagery.<br />
* We will be integrating the framework into Slicer3 and testing it on DWI data for Cingulum Bundle Segmentation.<br />
<br />
</div><br />
<br />
<div style="width: 40%; float: left;"><br />
<br />
<h3>Progress</h3><br />
* The framework has been implemented and tested successfully in MATLAB (with mexed C code).<br />
<br />
</div><br />
</div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2009_Summer_Project_Week&diff=388612009 Summer Project Week2009-06-17T17:58:13Z<p>Vandymohan: /* Projects */</p>
<hr />
<div>Back to [[Project Events]], [[Events]]<br />
<br />
[[Image:PW2009-v3.png|300px]]<br />
<br />
*'''Dates:''' June 22-26, 2009<br />
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].<br />
<br />
<br />
==Introduction to the FIRST JOINT PROJECT WEEK==<br />
<br />
We are pleased to announce the FIRST JOINT PROJECT WEEK of hands-on research and development activity for Image-Guided Therapy and Neuroscience applications. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. <br />
<br />
Active preparation will begin on''' Thursday, April 16th at 3pm ET''', with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects. <br />
<br />
The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 30-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.<br />
<br />
This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January. <br />
<br />
A summary of all past NA-MIC Project Events that this FIRST JOINT EVENT is based on is available [[Project_Events#Past|here]].<br />
<br />
== Agenda==<br />
* Monday <br />
** noon-1pm lunch <br />
**1pm: Welcome (Ron Kikinis)<br />
** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template Wiki Template]) <br />
** 3:30-5:30pm Start project work<br />
* Tuesday <br />
** 8:30am breakfast<br />
**9:30-10am: NA-MIC Kit Overview (Jim Miller)<br />
** 10-10:30am Slicer 3.4 Update (Steve Pieper)<br />
** 10:30-11am Slicer IGT and Imaging Kit Update Update (Noby Hata, Scott Hoge)<br />
** 11am-12:00pm Breakout Session: [[2009 Project Week Breakout Session: Slicer-Python]] (Demian W)<br />
** noon lunch<br />
** 2:30pm-4pm: [[2009 Project Week Data Clinic|Data Clinic]] (Ron Kikinis)<br />
** 4:30pm CIMIT Forum (at BWH Shapiro Center) Open Source Software for Translational IGT Research and Commercial Use, Clif Burdette, Acoustic MedSystems, Inc.<br />
At BWH / Carl J. and Ruth Shapiro Cardiovascular Cente<br />
<br />
** 5:30pm adjourn for day<br />
* Wednesday <br />
** 8:30am breakfast<br />
** 9am-12pm Breakout Session: [[2009 Project Week Breakout Session: ITK]] (Luis Ibanez)<br />
** noon lunch<br />
** 2:30pm: Breakout Session: [[2009 Project Week Breakout Session: 3D+T Microscopy Cell Dataset Segmentation]] (Alex G.)<br />
** 5:30pm adjourn for day<br />
* Thursday<br />
** 8:30am breakfast<br />
** 9-11am [[Events:TutorialContestJune2009|Tutorial Contest Presentations]]<br />
** noon lunch<br />
** 2:30pm: Breakout Session: [[2009 Project Week Breakout Session: XNAT for Programmers]] (Dan M.)<br />
** 5:30pm adjourn for day<br />
* Friday <br />
** 8:30am breakfast<br />
** 10am-noon: [[Events:TutorialContestJune2009|Tutorial Contest Winner Announcement]] and [[#Projects|Project Progress Updates]]<br />
*** Noon: Lunch boxes and adjourn by 1:30pm.<br />
***We need to empty room by 1:30. You are welcome to use wireless in Stata.<br />
***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]<br />
***Next Project Week [[AHM_2010|in Utah, January 4-8, 2010]]<br />
<br />
== Projects ==<br />
<br />
#[[2009_Summer_Project_Week_Slicer3_Cortical_Thickness_Pipeline|Cortical Thickness Pipeline]] (Clement Vachet UNC)<br />
#[[2009_Summer_Project_Week_Lupus_Lesion_Segmentation |Lupus Lesion Segmentation]] (Jeremy Bockholt MRN)<br />
#[[Summer2009:VCFS| Pipeline development for VCFS]] (Marek Kubicki BWH)<br />
#[[2009_Summer_Project_Week_Prostate_Robotics |Prostate Robotics]] (Junichi Tokuda BWH)<br />
#[[2009_Summer_Project_Week_Project_Segmentation_of_Muscoskeletal_Images]] (Harish Doddi Stanford)<br />
#[[2009_Summer_Project_Week_Liver_Ablation_Slicer|Liver Ablation in Slicer]] (Ziv Yaniv Georgetown)<br />
#[[Measuring Alcohol Stress Interaction]] (Vidya Rajgopalan Virginia Tech)<br />
#[[2009_Summer_Project_Week_WML_SEgmentation |White Matter Lesion segmentation]] (Minjeong Kim UNC)<br />
#[[2009_Summer_Project_Week_Skull_Stripping | Skull Stripping]] (Snehasish Roy JHU)<br />
# [[MeshingSummer2009 | IAFE Mesh Modules - improvements and testing]] (Curt Lisle Knowledge Vis)<br />
#[[2009_Summer_Project_Week_Slicer3_Adaptive_Radiotherapy|Adaptive Radiotherapy - Deformable registration and DICOMRT]] (Greg Sharp MGH)<br />
#[[2009_Summer_Project_Week_Slicer3_Brainlab_Introduction|SLicer3, BioImage Suite and Brainlab - Introduction and Demo to UCLA]] (Haiying Liu BWH)<br />
#[[2009_Summer_Project_Week_Multimodal_SPL_Brain_Atlas|Segmentation of thalamic nuclei from DTI]] (Ion-Florin Talos BWH)<br />
#[[2009_Summer_Project_Week_Slicer3_Fibre_Dispersion|Slicer module for the computation of fibre dispersion and curving measures]] (Peter Savadjiev BWH)<br />
#[[2009_Summer_Project_Week_Hageman_FMTractography | Fluid mechanics tractography and visualization]] (Nathan Hageman UCLA)<br />
#[[2009_Summer_Project_Week_DWI_/_DTI_QC_and_Prepare_Tool:_DTIPrep | DWI/DTI QC and Preparation Tool: DTIPrep]] (Zhexing Liu UNC)<br />
#[[2009_Summer_Project_Week_Hageman_DTIDigitalPhantom | DTI digital phantom generator to create validation data sets - webservice/cmdlin module/binaries are downloadable from UCLA ]] (Nathan Hageman UCLA)<br />
# [[EPI Correction in Slicer3 | EPI Correction in Slicer3]] (Ran Tao Utah)<br />
#[[2009_Summer_Project_Week-FastMarching_for_brain_tumor_segmentation |FastMarching for brain tumor segmentation]] (Andrey Fedorov BWH)<br />
#[[EMSegment|EM Segment]] (Sylvain Jaume MIT, Nicolas Rannou BWH)<br />
#[[2009_Summer_Project_Week_Meningioma_growth_simulation|Meningioma growth simulation]] (Andrey Fedorov BWH)<br />
#[[2009_Summer_Project_Week_Automatic_Brain_MRI_Pipeline|Automatic brain MRI processing pipeline]] (Marcel Prastawa Utah)<br />
#[[2009_Summer_Project_Week_HAMMER_Registration | HAMMER Registration]] (Guorong Wu UNC)<br />
#[[2009_Summer_Project_Week_Spherical_Mesh_Diffeomorphic_Demons_Registration |Spherical Mesh Diffeomorphic Demons Registration]] (Luis Ibanez Kitware)<br />
# [[BSpline Registration in Slicer3 | BSpline Registration in Slicer3]] (Samuel Gerber Utah)<br />
#[[2009_Summer_Project_Week_4D_Imaging| 4D Imaging (Perfusion, Cardiac, etc.) ]] (Junichi Tokuda BWH)<br />
#[[2009_Summer_Project_Week_MRSI-Module|MRSI Module]] (Bjoern Menze MIT)<br />
#[[2009_Summer_Project_Week_4D_Gated_US_In_Slicer |Gated 4D ultrasound reconstruction for Slicer3]] (Danielle Pace Robarts Institute)<br />
# [[Integration of stereo video into Slicer3]] (Mehdi Esteghamatian Robarts Institute)<br />
#[[2009_Summer_Project_Week_Statistical_Toolbox |multi-modality statistical toolbox for MR T1, T2, fMRI, DTI data]] (Diego Cantor Robarts Institute)<br />
# [[Summer2009:Using_ITK_in_python| Using ITK in python]] (Steve Pieper BWH)<br />
# [[Summer2009:Implementing_parallelism_in_python| Taking advantage of multicore machines & clusters with python]] (Julien de Siebenthal BWH)<br />
# [[Summer2009:Using_client_server_paradigm_with_python_and_slicer| Deferring heavy computational tasks with Slicer python]] (Julien de Siebenthal BWH)<br />
# [[Summer2009:Using_cython| Accelerating python with cython: application to stochastic tractography]] (Julien de Siebenthal BWH)<br />
# [[2009_Summer_Project_Week_VTK_3D_Widgets_In_Slicer3|VTK 3d Widgets in Slicer3]] (Nicole Aucoin BWH)<br />
# [[2009_Summer_Project_Week_Colors_Module |Updates to Slicer3 Colors module]] (Nicole Aucoin BWH)<br />
# [[Plug-In 3D Viewer based on XIP|Plug-in 3D Viewer based on XIP]] (Lining Yang Siemens Research)<br />
# [[Slicer3 Informatics Workflow Design & XNAT updates | Slicer3 Informatics Workflow Design & XNAT updates for Slicer]] (Wendy Plesniak BWH)<br />
# [[Summer2009:Registration reproducibility in Slicer|Registration reproducibility in Slicer3]] (Andrey Fedorov BWH)<br />
# [[Summer2009:The Vascular Modeling Toolkit in 3D Slicer | The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn BWH)<br />
# [[Summer2009:Extension of the Command Line XML Syntax/Interface | Extension of the Command Line XML Syntax/Interface]] (Bennett Landman)<br />
#[[2009_Summer_Project_Week_Slicer3_XNAT_UI | XNAT user interface improvements for NA-MIC]] (Dan Marcus WUSTL)<br />
#[[2009_Summer_Project_Week_XNATFS | XNAT File System with FUSE]] (Dan Marcus WUSTL)<br />
#[[2009_Summer_Project_Week_XNAT_i2b2|XNAT integration into Harvard Catalyst i2b2 framework]] (Yong Harvard)<br />
#[[2009_Summer_Project_Week_GWE_XNAT | GWE-XNAT Integration]] (Marco Ruiz UCSD)<br />
#[[2009_Summer_Project_Week_Slicer3_registration| Slicer 3 registration ]] (Andrew Rausch)<br />
#[[2009_Summer_Project_Week_Transrectal_Prostate_biopsy|Transrectal Prostate Biopsy]] (Andras Lasso Queen's)<br />
#[[2009_Summer_Project_Week_3DGRASE|3D GRASE]] (Scott Hoge BWH)<br />
#[[2009_Summer_Project_Week_TrigeminalNerve|Atlas to CT Registration in Trigeminal Neuralgia]] (Marta Peroni PoliMI, Maria Francesca Spadea UMG, Greg Sharp MGH)<br />
#[[2009_Summer_Project_Week_FunctionalClusteringAnalysis|Functional Analysis of White Matter in Whole Brain Clustering of Schizophrenic Patients]] (Doug Terry, Marek Kubicki BWH)<br />
#[[2009_Summer_Project_Week_Slicer|Integration of Flexible Surgical Instrument Modeling and Virtual Catheter with Slicer]] (Jayender Jagadeesan BWH)<br />
#[[2009_Summer_Project_Week_Orthogonal_Reformat_Widget|Orthogonal Planes in Reformat Widget]] (Michal Depa MIT)<br />
#[[2009_Summer_Project_Week_New_ITK_Level_Set_Framework|New Level Framework in ITK]] (Arnaud Gelas, Harvard Medical School)<br />
#[[2009_Summer_Project_Week_TubularSurfaceSeg|Tubular Surface Segmentation in Slicer]] (Vandana Mohan, Georgia Tech)<br />
<br />
===CUDA Projects===<br />
<br />
This is a list of candidate cuda projects that will be discussed with Joe Stam shortly:<br />
<br />
#[[2009_Summer_Project_Week_Registration_for_RT|2d/3d Registration (and GPGPU acceleration) for Radiation Therapy]] (Tina Kapur BWH)<br />
#[[2009_Summer_Project_Week_Statistical_Toolbox |multi-modality statistical toolbox for MR T1, T2, fMRI, DTI data]] (Diego Cantor Robarts Institute)<br />
#[[2009_Summer_Project_Week_Dose_Calculation |accelerate calculation for LDR seeds]] (Jack Blevins Acousticmed)<br />
#[[2009_Summer_Project_Week_Cone_Beam_backprojection]](Zhou Shen U Michigan)<br />
#[[2009_Summer_project_week_3d_Deformable_alignment]](Dan McShan U Michigan)<br />
#[[Summer2009:Using_CUDA_for_stochastic_tractography|Developing interactive stochastic tractography using CUDA]] (Julien de Siebenthal BWH)<br />
#acceleration of parallel real time processing of strain and elasticity images for monitoring of ablative therapy (Clif Burdette Acousticmed)<br />
<br />
== Preparation ==<br />
<br />
# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list<br />
# Join the kickoff TCON on April 16, 3pm ET.<br />
# [[Engineering:TCON_2009|June 18 TCON]] at 3pm ET to tie loose ends. Anyone with un-addressed questions should call.<br />
# By 3pm ET on June 11, 2009: [[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.<br />
# By 3pm on June 18, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)<br />
## 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)<br />
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)<br />
## 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)<br />
# 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...<br />
# People doing Slicer related projects should come to project week with slicer built on your laptop.<br />
## Projects to develop extension modules should work with the [http://viewvc.slicer.org/viewcvs.cgi/branches/Slicer-3-4/#dirlist Slicer-3-4 branch] (new code should not be checked into the branch).<br />
## Projects to modify core behavior of slicer should be done on the [http://viewvc.slicer.org/viewcvs.cgi/trunk/ trunk].<br />
<br />
==Attendee List==<br />
If you plan to attend, please add your name here.<br />
<br />
#Ron Kikinis, BWH (NA-MIC, NAC, NCIGT)<br />
#Clare Tempany, BWH (NCIGT)<br />
#Tina Kapur, BWH (NA-MIC, NCIGT)<br />
#Steve Pieper, Isomics Inc<br />
#Jim Miller, GE Research<br />
#Xiaodong Tao, GE Research<br />
#Randy Gollub, MGH<br />
#Nicole Aucoin, BWH (NA-MIC) (Attending Tuesday-Friday)<br />
#Dan Marcus, WUSTL<br />
#Junichi Tokuda, BWH (NCIGT)<br />
#Alex Gouaillard, Harvard Systems Biology<br />
#Arnaud Gelas, Harvard Systems Biology <br />
#Kishore Mosanliganti, Harvard Systems Biology<br />
#Lydie Souhait, Harvard Systems Biology<br />
#Luis Ibanez, Kitware Inc (Attending: Monday/Tuesday/Wednesday)<br />
#Vincent Magnotta, UIowa<br />
#Hans Johnson, UIowa<br />
#Xenios Papademetris, Yale<br />
#Gregory S. Fischer, WPI (Mon, Tue, Wed)<br />
#Daniel Blezek, Mayo (Tue-Fri)<br />
#Danielle Pace, Robarts Research Institute / UWO<br />
#Clement Vachet, UNC-Chapel Hill<br />
#Dave Welch, UIowa<br />
#Demian Wassermann, Odyssée lab, INRIA, France<br />
#Manasi Ramachandran, UIowa<br />
#Greg Sharp, MGH<br />
#Rui Li, MGH<br />
#Mehdi Esteghamatian, Robarts Research Institute / UWO<br />
#Misha Milchenko, WUSTL<br />
#Kevin Archie, WUSTL<br />
#Tim Olsen, WUSTL<br />
#Wendy Plesniak BWH (NAC)<br />
#Haiying Liu BWH (NCIGT)<br />
#Curtis Lisle, KnowledgeVis / Isomics<br />
#Diego Cantor, Robarts Research Institute / UWO<br />
#Daniel Haehn, BWH<br />
#Nicolas Rannou, BWH<br />
#Sylvain Jaume, MIT<br />
#Alex Yarmarkovich, Isomics<br />
#Marco Ruiz, UCSD<br />
#Andriy Fedorov, BWH (NA-MIC)<br />
#Harish Doddi, Stanford University<br />
#Scott Hoge, BWH (NCIGT)<br />
#Vandana Mohan, Georgia Tech<br />
#Ivan Kolosev, Georgia Tech<br />
#Behnood Gholami, Georgia Tech<br />
#James Balter, U Michigan<br />
#Dan McShan, U Michigan<br />
#Zhou Shen, U Michigan<br />
#Maria Francesca Spadea, Italy<br />
#Lining Yang, Siemens Corporate Research<br />
#Beatriz Paniagua, UNC-Chapel Hill<br />
#Bennett Landman, Johns Hopkins University <br />
#Snehashis Roy, Johns Hopkins University<br />
#Marta Peroni, Politecnico di Milano<br />
#Sebastien Barre, Kitware, Inc.<br />
#Samuel Gerber, SCI University of Utah<br />
#Ran Tao, SCI University of Utah<br />
#Marcel Prastawa, SCI University of Utah<br />
#Katie Hayes, BWH (NA-MIC)<br />
#Sonia Pujol, BWH (NA-MIC)<br />
#Andras Lasso, Queen's University<br />
#Yong Gao, MGH<br />
#Minjeong Kim, UNC-Chapel Hill<br />
#Guorong Wu, UNC-Chapel Hill<br />
#Jeffrey Yager, UIowa<br />
#Yanling Liu, SAIC/NCI-Frederick<br />
#Ziv Yaniv, Georgetown<br />
#Bjoern Menze, MIT<br />
#Vidya Rajagopalan, Virginia Tech<br />
#Sandy Wells, BWH (NAC, NCIGT)<br />
#Lilla Zollei, MGH (NAC)<br />
#Lauren O'Donnell, BWH<br />
#Florin Talos, BWH (NAC)<br />
#Nobuhiko Hata, BWH (NCIGT)<br />
#Alark Joshi, Yale<br />
#Yogesh Rathi, BWH<br />
#Jimi Malcolm, BWH<br />
#Dustin Scheinost, Yale<br />
#Dominique Belhachemi, Yale<br />
#Sam Song, JHU<br />
#Nathan Cho, JHU<br />
#Julien de Siebenthal, BWH<br />
#Peter Savadjiev, BWH<br />
#Carl-Fredrik Westin, BWH<br />
#John Melonakos, AccelerEyes (Wed & Thu morning)<br />
#Yi Gao, Georgia Tech<br />
#Sylvain Bouix, BWH<br />
#Zhexing Liu, UNC-CH<br />
#Eric Melonakos, BWH<br />
#Lei Qin, BWH<br />
#Giovanna Danagoulian, BWH<br />
#Andrew Rausch, BWH (Monday)<br />
#Haytham Elhawary, BWH<br />
#Jayender Jagadeesan, BWH<br />
#Marek Kubicki, BWH<br />
#Doug Terry, BWH<br />
#Nathan Hageman, LONI (UCLA)<br />
#Dana Peters, Beth Israel Deaconess<br />
#Sun Woo Lee, BWH<br />
# Melanie Grebe, Siemens Corporate Research<br />
# Megumi Nakao, BWH/NAIST<br />
# Moti Freiman, The Hebrew Univ. of Jerusalem<br />
#Jack Blevins, Acoustic Med Systems<br />
#Michael Halle, BWH<br />
#Amanda Peters, Harvard SEAS<br />
#Joe Stam, NVIDIA (Wednesday, Thursday)<br />
#Petter Risholm, BWH (NCIGT)<br />
#Kimberly Powell, NVIDIA (Wednesday)<br />
#Padma Akella, BWH (NCIGT)<br />
#Clif Burdette, Acousticmed (Mon, Tue, Wed)<br />
#Mark Scully, MRN<br />
#Jeremy Bockholt, MRN (tues-thurs)<br />
#Curtis Rueden, UW-Madison<br />
#Juhana Frosen, BWH (Tuesday)<br />
#Andrzej Przybyszewski, UMass Medical School (Monday)<br />
#Robert Yaffe, MGH<br />
#Kenneth (Cal) Hisley, Des Moines University<br />
#Ross Whitaker, University of Utah (Wed-Fri)<br />
#Michal Depa, MIT<br />
<br />
== Logistics ==<br />
*'''Dates:''' June 22-26, 2009<br />
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].<br />
*'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week). Due by Friday, June 12th, 2009. Please make checks out to "Massachusetts Institute of Technology" and mail to: Donna Kaufman, MIT, 77 Massachusetts Ave., 38-409a, Cambridge, MA 02139. Receipts will be provided by email as checks are received. Please send questions to dkauf at mit.edu. '''If this is your first event and you are attending for only one day, the registration fee is waived.''' Please let us know, so that we can cover the costs with one of our grants.<br />
*'''Registration Method''' Add your name to the Attendee List section of this page<br />
*'''Hotel:''' We have a group rate of $189/night (plus tax) at the Le Meridien (which used to be the Hotel at MIT). [http://www.starwoodmeeting.com/Book/MITDECSE Please click here to reserve.] This rate is good only through June 1.<br />
*Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.<br />
*2009 Summer Project Week [[NA-MIC/Projects/Theme/Template|'''Template''']]<br />
*[[2008_Summer_Project_Week#Projects|Last Year's Projects as a reference]]<br />
*For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]<br />
<br />
==Testing Sortable Tables==<br />
<br />
{|class="wikitable sortable"<br />
!demo<br />
|-<br />
|9<br />
|-<br />
|12<br />
|-<br />
|11<br />
|}</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Algorithm:GATech&diff=37060Algorithm:GATech2009-05-07T13:02:57Z<p>Vandymohan: /* Georgia Tech Projects */</p>
<hr />
<div>Back to [[Algorithm:Main|NA-MIC Algorithms]]<br />
__NOTOC__<br />
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =<br />
<br />
At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.<br />
<br />
= Georgia Tech Projects =<br />
<br />
{| cellpadding="10"<br />
<br />
| style="width:15%" | [[Image:Results brain sag.JPG|200px|]]<br />
| style="width:85%" |<br />
<br />
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==<br />
<br />
The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]<br />
<br />
<font color="red">'''New: '''</font> Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. SPIE Conference on Computational Imaging V, Jan 2007.<br />
<br />
|-<br />
<br />
| | [[Image:ShapeBasePstSegSlicer.png|200px|]]<br />
| |<br />
<br />
== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==<br />
<br />
The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Segmentation tool put into Slicer3.<br />
<br />
<br />
|-<br />
<br />
| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]<br />
| |<br />
<br />
== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==<br />
<br />
3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]<br />
<br />
<font color="red">'''New: '''</font> Will be put into Slicer3.<br />
<br />
|-<br />
<br />
| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]<br />
| |<br />
<br />
== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==<br />
<br />
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==<br />
<br />
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]<br />
<br />
<br />
|-<br />
<br />
| | [[Image:Gatech caudateBands.PNG|200px]]<br />
| |<br />
<br />
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==<br />
<br />
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Delphine Nain won the best student paper at [[MICCAI_2006|MICCAI 2006]] in the category "Segmentation and Registration" for her paper entitled "Shape-driven surface segmentation using spherical wavelets" by D. Nain, S. Haker, A. Bobick, A. Tannenbaum.<br />
<br />
|-<br />
<br />
| | [[Image:ZoomedResultWithModel.png|200px]]<br />
| |<br />
<br />
== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==<br />
<br />
In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.<br />
<br />
|-<br />
<br />
| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]<br />
| |<br />
<br />
== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==<br />
<br />
Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]<br />
<br />
<font color="red">'''New: '''</font> Bayesian Spherical Wavelet Shrinkage:Applications to shape analysis, X. Le Faucheur, B. Vidakovic, A. Tannenbaum, Proc. of SPIE Optics East, 2007.<br />
<br />
|-<br />
<br />
| | [[Image:P1_small.png|200px|]]<br />
| |<br />
<br />
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==<br />
<br />
Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space\: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.<br />
<br />
|-<br />
<br />
| | [[Image:BasePair3DModel.JPG|200px|]]<br />
| |<br />
<br />
== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==<br />
<br />
High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]<br />
<br />
<font color="red">'''New: '''</font> E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. To be published in Macromolecules. 2008.<br />
<br />
|-<br />
<br />
| | [[Image:Basis membership.png|200px]]<br />
| |<br />
<br />
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==<br />
<br />
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]<br />
<br />
<font color="red">'''New: '''</font> D. Nain, M. Styner, M. Niethammer, J. J. Levitt, M E Shenton, G Gerig, A. Bobick, A. Tannenbaum. Statistical Shape Analysis of Brain Structures using Spherical Wavelets. Accepted in The Fourth IEEE International Symposium on Biomedical Imaging (ISBI ’07) that will be held April 12-15, 2007 in Metro Washington DC, USA.<br />
<br />
|-<br />
<br />
| | [[Image:TruckInitialization.png|200px|]]<br />
| |<br />
<br />
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==<br />
<br />
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by<br />
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]<br />
<br />
<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.<br />
<br />
|-<br />
<br />
| | [[Image:Dlpfc1.jpg|200px|]]<br />
| |<br />
<br />
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Al-Hakim, et al. A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter. SPIE MI 2006.<br />
<br />
|-<br />
<br />
| | [[Image:Striatum1.png|200px|]]<br />
| |<br />
<br />
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007.<br />
<br />
|-<br />
<br />
| | [[Image:Brain-flat.PNG|200px]]<br />
| |<br />
<br />
== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==<br />
<br />
The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]<br />
<br />
<font color="red">'''New: '''</font> Y. Gao, J. Melonakos, and A. Tannenbaum. Conformal Flattening ITK Filter. ISC/NA-MIC Workshop on Open Science at MICCAI 2006.<br />
<br />
|-<br />
<br />
| | [[Image:Circle seg.PNG|200px|]]<br />
| |<br />
<br />
== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==<br />
<br />
Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. PAMI. Submitted to PAMI.<br />
<br />
|-<br />
<br />
| | [[Image:Fig1yan.PNG|200px|]]<br />
| |<br />
<br />
== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==<br />
<br />
The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font>Y. Yang, S. George, D. Martin, A. Tannenbaum, and D. Giddens. 3D Modeling of Patient-Specific Geometries of Portal Veins Using MR Images. In Proceedings IEEE EMBS, 2006<br />
<br />
|-<br />
<br />
| | [[Image:Fig67.png|200px|]]<br />
| |<br />
<br />
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==<br />
<br />
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Melonakos, Y. Gao, and A. Tannenbaum. Tissue Tracking: Applications for Brain MRI Classification. SPIE Medical Imaging, 2007.<br />
<br />
|-<br />
<br />
| | [[Image:Stochastic-snake.png|200px|]]<br />
| |<br />
<br />
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==<br />
<br />
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Currently under investigation.<br />
<br />
|-<br />
<br />
| | [[Image:GT-SulciOutlining1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==<br />
<br />
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]<br />
<br />
|-<br />
<br />
| | [[Image:Table1.png|200px|]]<br />
| |<br />
<br />
== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==<br />
<br />
The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]<br />
<br />
<font color="red">'''New: '''</font> Y. Rathi, S. Dambreville, and A. Tannenbaum. "Comparative Analysis of Kernel Methods for Statistical Shape Learning", In CVAMIA held in conjunction with ECCV, 2006.<br />
<br />
|-<br />
<br />
| | [[Image:Gatech SlicerModel2.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==<br />
<br />
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]<br />
<br />
|}</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=2009_Summer_Project_Week&diff=369652009 Summer Project Week2009-05-02T15:49:04Z<p>Vandymohan: /* Attendee List */</p>
<hr />
<div>Back to [[Project Events]], [[Events]]<br />
<br />
*'''Dates:''' June 22-26, 2009<br />
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].<br />
<br />
<br />
==Introduction to the FIRST JOINT PROJECT WEEK==<br />
<br />
We are pleased to announce the FIRST JOINT PROJECT WEEK of hands-on research and development activity for Image-Guided Therapy and Neuroscience applications. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. <br />
<br />
Active preparation will begin on''' Thursday, April 16th at 3pm ET''', with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects. <br />
<br />
The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 30-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.<br />
<br />
This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January. <br />
<br />
A summary of all past NA-MIC Project Events that this FIRST JOINT EVENT is based on is available [[Project_Events#Past|here]].<br />
<br />
== Agenda==<br />
* Monday <br />
** noon-1pm lunch <br />
**1pm: Welcome (Ron Kikinis)<br />
** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([[NA-MIC/Projects/Theme/Template|Wiki Template]]) <br />
** 3:30-5:30pm Start project work<br />
* Tuesday <br />
** 8:30am breakfast<br />
**9:30-10am: NA-MIC Kit Overview (Jim Miller)<br />
** 10-10:30am Slicer 3.4 Update (Steve Pieper)<br />
** 10:30-11am Slicer IGT and Imaging Kit Update Update (Noby Hata, Scott Hoge)<br />
** 11am-12:00pm Breakout Session: [[2009 Project Week Breakout Session: Slicer-Python]] (Demian W)<br />
** noon lunch<br />
** 2:30pm-5pm: [[2009 Project Week Data Clinic|Data Clinic]]<br />
** 5:30pm adjourn for day<br />
* Wednesday <br />
** 8:30am breakfast<br />
** 9am-12pm Breakout Session: [[2009 Project Week Breakout Session: ITK]] (Luis Ibanez)<br />
** noon lunch<br />
** 2:30pm: Breakout Session: [[2009 Project Week Breakout Session: 4D+T Microscopy Cell Dataset Segmentation]] (Alex G.)<br />
** 5:30pm adjourn for day<br />
* Thursday<br />
** 8:30am breakfast<br />
** 9-11pm Tutorial Contest Presentations<br />
** noon lunch<br />
** 2:30pm: Breakout Session: TBD<br />
** 5:30pm adjourn for day<br />
* Friday <br />
** 8:30am breakfast<br />
** 10am-noon: Tutorial Contest Winner Announcement and Project Progress using update [[#Projects|Project Wiki pages]]<br />
*** Noon: Lunch boxes and adjourn by 1:30pm.<br />
***We need to empty room by 1:30. You are welcome to use wireless in Stata.<br />
***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]<br />
***Next Project Week [[AHM_2010|in Utah, January 4-8, 2010]]<br />
<br />
== Projects ==<br />
<br />
The list of projects for this week will go here.<br />
<br />
*Prostate Robotics (Junichi, Sam, Nathan Cho, Jack), - Mon, Tue, Thursday 7pm-midnight)<br />
*4D Imaging - currently used for Lung Perfusion (Junichi, Dan Blezek?, Steve, Alex G?)<br />
*Liver Ablation in Slicer (Haiying, Georgetown?)<br />
*SLicer3 and Brainlab - introduction to UCLA (Haiying, Xenios, Pratik, Nathan Hageman)<br />
*Adaptive Radiotherapy - Deformable registration and DICOMRT (Greg Sharp, Steve, Wendy)<br />
*gpu based registration acceleration (James Balter, Greg Sharp, Alark Joshi?, Aditya K., Yogesh Rathi?, Jimi Malcolm, Sandy Wells, Tina Kapur)<br />
*Brain DTI Atlas? (Florin, Utah, UNC, GeorgiaTech)<br />
*Xnat user interface improvements for NA-MIC (Dan M, Tina, Florin, Ron, Wendy)<br />
*xnat and DICOMRT (Greg Sharp, Dan M) - might be done?<br />
*Xnat user clinic - combine with data clinic<br />
*xnat programmer clinic<br />
*Grid Wizard+xnat clinic (Clement)<br />
*?Fluid Mechanincs Module (Nathan Hageman)<br />
*?DTI digital phantom generator to create validation data sets - webservice/cmdlin module/binaries are downloadable from UCLA (Nathan Hageman)<br />
*Cortical Thickness Pipeline (Clement, Ipek)<br />
*Demo Brainlab/Slicer in BWH OR (Haiying, Nathan Hageman)<br />
<br />
IGT Projects:<br />
*port 4d gated ultrasound code to Slicer - (Danielle)<br />
*integration of stereo video into Slicer (Mehdi)<br />
*multi-modality statistical toolbox for MR T1, T2, fMRI, DTI data (Diego, sylvain jaume, nicholas, noby)<br />
*neuroendoscope workflow presentation (sebastien barre)<br />
*slicer integration of mri compatible prostate biopsy robot(sid, queens)<br />
*breakout session on Dynamic Patient Models (James Balter)<br />
*gpu acceleration of 2d-3d registration (james balter, greg sharp, sandy wells, noby hata, terry peters proxy)<br />
<br />
NA-MIC Engineering Projects<br />
* DICOM Validation and Cleanup Tool (Luis, Sid, Steve, Greg)<br />
* Using ITK in python (Steve, Demian, Jim)<br />
* VTK 3d Widgets in Slicer3 (Nicole, Will/Karthik)<br />
* Update to Slicer3 Colors module (Nicole)<br />
* EM Segmenter (Sylvain, Nicolas)<br />
<br />
== Preparation ==<br />
<br />
# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list<br />
# Join the kickoff TCON on April 16, 3pm ET.<br />
# [[Engineering:TCON_2009|June 18 TCON]] at 3pm ET to tie loose ends. Anyone with un-addressed questions should call.<br />
# By 3pm ET on June 11, 2009: [[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.<br />
# By 3pm on June 18, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)<br />
## 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)<br />
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)<br />
## 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)<br />
# 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...<br />
<br />
==Attendee List==<br />
If you plan to attend, please add your name here.<br />
<br />
#Ron Kikinis, BWH<br />
#Ferenc Jolesz, BWH<br />
#Clare Tempany, BWH<br />
#Tina Kapur, BWH<br />
#Steve Pieper, Isomics Inc<br />
#Jim Miller, GE Research<br />
#Xiaodong Tao, GE Research<br />
#Bill Lorensen, EAB<br />
#Randy Gollub, MGH<br />
#Nicole Aucoin, BWH<br />
#Dan Marcus, WUSTL<br />
#Junichi Tokuda, BWH<br />
#Alex Gouaillard, Harvard Systems Biology<br />
#Arnaud Gelas, Harvard Systems Biology <br />
#Kishore Mosanliganti, Harvard Systems Biology<br />
#Lydie Souhait, Harvard Systems Biology<br />
#Luis Ibanez, Kitware Inc<br />
#Vincent Magnotta, UIowa<br />
#Xenios Papademetris, Yale<br />
#Gregory S. Fischer, WPI (Mon, Tue, Wed)<br />
#Daniel Blezek, Mayo (Tue-Fri)<br />
#Danielle Pace, Robarts Research Institute / UWO<br />
#Clement Vachet, UNC-Chapel Hill<br />
#Dave Welch, UIowa<br />
#Demian Wassermann, Odyssée lab, INRIA, France<br />
#Manasi Ramachandran, UIowa<br />
#Greg Sharp, MGH<br />
#Rui Li, MGH<br />
#Mehdi Esteghamatian, Robarts Research Institute / UWO<br />
#Misha Milchenko, WUSTL<br />
#Kevin Archie, WUSTL<br />
#Tim Olsen, WUSTL<br />
#Wendy Plesniak BWH<br />
#Haiying Liu BWH<br />
#Curtis Lisle, KnowledgeVis / Isomics<br />
#Diego Cantor, Robarts Research Institute / UWO<br />
#Daniel Haehn, BWH<br />
#Nicolas Rannou, BWH<br />
#Sylvain Jaume, MIT<br />
#Alex Yarmarkovich, Isomics<br />
#Marco Ruiz, UCSD<br />
#Andriy Fedorov, BWH<br />
#Harish Doddi, Stanford University<br />
#Saikat Pal, Stanford University<br />
#Scott Hoge, BWH<br />
#Vandana Mohan, Georgia Tech<br />
#Ivan Kolosev, Georgia Tech<br />
#Behnood Gholami, Georgia Tech<br />
<br />
== Logistics ==<br />
*'''Dates:''' June 22-26, 2009<br />
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].<br />
*'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week). Due by Friday, June 12th, 2009. Please make checks out to "Massachusetts Institute of Technology" and mail to: Donna Kaufman, MIT, 77 Massachusetts Ave., 38-409a, Cambridge, MA 02139. Receipts will be provided by email as checks are received. Please send questions to dkauf at mit.edu. '''If this is your first event and you are attending for only one day, the registration fee is waived.''' Please let us know, so that we can cover the costs with one of our grants.<br />
*'''Registration Method''' Add your name to the Attendee List section of this page<br />
*'''Hotel:''' We have a group rate of $189/night (plus tax) at the Le Meridien (which used to be the Hotel at MIT). [http://www.starwoodmeeting.com/Book/MITDECSE Please click here to reserve.] This rate is good only through June 1.<br />
*Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.<br />
*2009 Summer Project Week [[NA-MIC/Projects/Theme/Template|'''Template''']]<br />
*[[2008_Summer_Project_Week#Projects|Last Year's Projects as a reference]]<br />
*For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentation&diff=36318Projects:TubularSurfaceSegmentation2009-04-16T16:44:11Z<p>Vandymohan: </p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:GATech|Georgia Tech Algorithms]], [[Engineering:Kitware|Kitware Engineering]]<br />
__NOTOC__<br />
= Tubular Surface Segmentation Framework =<br />
<br />
This is a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. This model affords computation efficiency and stability (by the use of the Sobolev norm) and we have successfully tested its application in segmenting the Cingulum Bundle from DW-MRI of the brain as well as vessel segmentation from CT cardiac data.<br />
<br />
= Description =<br />
<br />
We have proposed a new model for tubular surfaces that represents a tubular surface as a center-line with a radius function associated with every point of the center-line. This transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels.<br />
<br />
''Some Results''<br />
<br />
* [[Image:GTTubSurfaceSeg-Img1.png | 3D visualization of CB segmentation| 300px]] 3D visualization of CB segmentation<br />
* [[Image:GTTubSurfaceSeg-Slice_Img1.png | Slice-wise view of CB Segmentation result | 300px]] Slice-wise view of CB Segmentation result<br />
* [[Image:GTTubSurfaceSeg-Slice_Img2.png | Slice-wise view of CB Segmentation result | 300px]] Slice-wise view of CB Segmentation result<br />
<br />
<br />
''Project Status''<br />
* Algorithm successfully implemented and tested in MATLAB for Cingulum Bundle (as well as vessel segmentation).<br />
<br />
= Key Investigators =<br />
<br />
* Georgia Tech Algorithms: Vandana Mohan, Allen Tannenbaum<br />
* Kitware Engineering: Luis Ibanez<br />
<br />
= Publications =<br />
*V. Mohan, G. Sundaramoorthi, J. Melonakos, M. Niethammer, M. Kubicki, and A. Tannenbaum. Tubular Surface Evolution for Segmentation of Tubular Structures with Applications to the Cingulum Bundle From DW-MRI. In Proceedings of MICCAI Workshop on Mathematical Foundations of Computational Anatomy, 2008.<br />
*V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for identifying anatomical structures from medical imagery. (In submission)<br />
<br />
[[Category: Segmentation]] [[Category:MRI]] [[Category:Slicer]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentation&diff=36317Projects:TubularSurfaceSegmentation2009-04-16T16:43:49Z<p>Vandymohan: </p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:GATech|Georgia Tech Algorithms]], [[Engineering:Kitware|Kitware Engineering]]<br />
__NOTOC__<br />
= Tubular Surface Segmentation Framework =<br />
<br />
This is a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. This model affords computation efficiency and stability (by the use of the Sobolev norm) and we have successfully tested its application in segmenting the Cingulum Bundle from DW-MRI of the brain as well as vessel segmentation from CT cardiac data.<br />
<br />
= Description =<br />
<br />
We have proposed a new model for tubular surfaces that represents a tubular surface as a center-line with a radius function associated with every point of the center-line. This transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels.<br />
<br />
''Some Results''<br />
<br />
* [[Image:GTTubSurfaceSeg-Img1.png | 3D visualization of CB segmentation| 600px]] 3D visualization of CB segmentation<br />
* [[Image:GTTubSurfaceSeg-Slice_Img1.png | Slice-wise view of CB Segmentation result | 600px]] Slice-wise view of CB Segmentation result<br />
* [[Image:GTTubSurfaceSeg-Slice_Img2.png | Slice-wise view of CB Segmentation result | 600px]] Slice-wise view of CB Segmentation result<br />
<br />
<br />
''Project Status''<br />
* Algorithm successfully implemented and tested in MATLAB for Cingulum Bundle (as well as vessel segmentation).<br />
<br />
= Key Investigators =<br />
<br />
* Georgia Tech Algorithms: Vandana Mohan, Allen Tannenbaum<br />
* Kitware Engineering: Luis Ibanez<br />
<br />
= Publications =<br />
*V. Mohan, G. Sundaramoorthi, J. Melonakos, M. Niethammer, M. Kubicki, and A. Tannenbaum. Tubular Surface Evolution for Segmentation of Tubular Structures with Applications to the Cingulum Bundle From DW-MRI. In Proceedings of MICCAI Workshop on Mathematical Foundations of Computational Anatomy, 2008.<br />
*V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for identifying anatomical structures from medical imagery. (In submission)<br />
<br />
[[Category: Segmentation]] [[Category:MRI]] [[Category:Slicer]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=Projects:TubularSurfaceSegmentation&diff=36316Projects:TubularSurfaceSegmentation2009-04-16T16:34:12Z<p>Vandymohan: Created page with ' Back to NA-MIC Collaborations, Georgia Tech Algorithms, [[Engineering:Kitware|Kitware Engineering...'</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:GATech|Georgia Tech Algorithms]], [[Engineering:Kitware|Kitware Engineering]]<br />
__NOTOC__<br />
= Tubular Surface Segmentation Framework =<br />
<br />
This is a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. This model affords computation efficiency and stability (by the use of the Sobolev norm) and we have successfully tested its application in segmenting the Cingulum Bundle from DW-MRI of the brain as well as vessel segmentation from CT cardiac data.<br />
<br />
= Description =<br />
<br />
We have proposed a new model for tubular surfaces that represents a tubular surface as a center-line with a radius function associated with every point of the center-line. This transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels.<br />
<br />
''Some Results''<br />
<br />
* [[Image:Plot GTTubSurfaceSeg-Img1.png | 3D visualization of CB segmentation| 600px]] 3D visualization of CB segmentation<br />
* [[Image:Plot GTTubSurfaceSeg-Slice_Img1.png | Slice-wise view of CB Segmentation result | 600px]] Slice-wise view of CB Segmentation result<br />
* [[Image:Plot GTTubSurfaceSeg-Slice_Img2.png | Slice-wise view of CB Segmentation result | 600px]] Slice-wise view of CB Segmentation result<br />
<br />
<br />
''Project Status''<br />
* Algorithm successfully implemented and tested in MATLAB for Cingulum Bundle (as well as vessel segmentation).<br />
<br />
= Key Investigators =<br />
<br />
* Georgia Tech Algorithms: Vandana Mohan, Allen Tannenbaum<br />
* Kitware Engineering: Luis Ibanez<br />
<br />
= Publications =<br />
*V. Mohan, G. Sundaramoorthi, J. Melonakos, M. Niethammer, M. Kubicki, and A. Tannenbaum. Tubular Surface Evolution for Segmentation of Tubular Structures with Applications to the Cingulum Bundle From DW-MRI. In Proceedings of MICCAI Workshop on Mathematical Foundations of Computational Anatomy, 2008.<br />
*V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for identifying anatomical structures from medical imagery. (In submission)<br />
<br />
[[Category: Segmentation]] [[Category:MRI]] [[Category:Slicer]]</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=File:GTTubSurfaceSeg-Slice_Img2.png&diff=36315File:GTTubSurfaceSeg-Slice Img2.png2009-04-16T16:25:46Z<p>Vandymohan: </p>
<hr />
<div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=File:GTTubSurfaceSeg-Slice_Img1.png&diff=36314File:GTTubSurfaceSeg-Slice Img1.png2009-04-16T16:25:22Z<p>Vandymohan: </p>
<hr />
<div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=NA-MIC_Internal_Collaborations:StructuralImageAnalysis&diff=36313NA-MIC Internal Collaborations:StructuralImageAnalysis2009-04-16T16:22:01Z<p>Vandymohan: /* Structural Image Analysis */</p>
<hr />
<div> Back to [[NA-MIC_Internal_Collaborations|NA-MIC Internal Collaborations]]<br />
__NOTOC__<br />
= Structural Image Analysis =<br />
<br />
=== Image Segmentation ===<br />
<br />
{| cellpadding="10"<br />
| style="width:15%" | [[Image:ProstateDiagram.png|200px]]<br />
| style="width:85%" |<br />
<br />
== [[ProjectWeek200706:BrachytherapyNeedlePositioningRobotIntegration|Brachytherapy Needle Positioning Robot Integration]] ==<br />
<br />
The Queen’s/Hopkins team is developing novel devices and procedures for cancer interventions, including biopsy and therapies. Our goal for the programming week is to design and start implementing software for the new MRI Brachytherapy needle positioning robot. [[ProjectWeek200706:BrachytherapyNeedlePositioningRobotIntegration|More...]]<br />
<br />
<font color="red">'''New: '''</font> Meeting at JHU on July 17-19, 2007.<br />
<br />
|-<br />
<br />
| | [[Image:Fig67.png|200px]]<br />
| |<br />
<br />
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==<br />
<br />
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> J. Melonakos, Y. Gao, and A. Tannenbaum. Tissue Tracking: Applications for Brain MRI Classification. SPIE Medical Imaging, 2007.<br />
<br />
|-<br />
<br />
| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]<br />
| |<br />
<br />
== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==<br />
<br />
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]<br />
<br />
<br />
|-<br />
<br />
| | [[Image:Striatum1.png|200px]]<br />
| |<br />
<br />
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font> Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007.<br />
<br />
|-<br />
<br />
| | [[Image:Dlpfc1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==<br />
<br />
In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]<br />
<br />
<font color="red">'''New: '''</font>Al-Hakim R, Nain D, Melonakos J, Tannenbaum A, Fallon J. [http://www.na-mic.org/pages/Special:PubDB_View?dspaceid=320 A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter.] Proc SPIE Medical Imaging, 2006. <br />
<br />
<br />
|-<br />
<br />
| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]<br />
| |<br />
<br />
== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==<br />
<br />
This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]<br />
<br />
<br />
<br />
|-<br />
<br />
| | [[Image:Gatech caudateBands.PNG|200px]]<br />
| |<br />
<br />
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==<br />
<br />
To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]<br />
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<font color="red">'''New: '''</font> Delphine Nain won the best student paper at [[MICCAI_2006|MICCAI 2006]] in the category "Segmentation and Registration" for her paper entitled "Shape-driven surface segmentation using spherical wavelets" by D. Nain, S. Haker, A. Bobick, A. Tannenbaum.<br />
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== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==<br />
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New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]<br />
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<font color="red">'''New: '''</font> Currently under investigation.<br />
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==<br />
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This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]<br />
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== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==<br />
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We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.<br />
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]<br />
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T Tasdizen, S Awate, R Whitaker, A nonparametric, entropy-minimizing MRI tissue classification algorithm implementation using ITK, MICCAI 2005 Open-Source Workshop.<br />
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== [[Projects:AutomaticFullBrainSegmentation|Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms]] ==<br />
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Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Projects:AutomaticFullBrainSegmentation|More...]]<br />
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<font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007<br />
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=== Image Registration ===<br />
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==<br />
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The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]<br />
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<font color="red">'''New: '''</font> Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. SPIE Conference on Computational Imaging V, Jan 2007.<br />
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== [[Projects:DTIProcessingTools|Diffusion Tensor Image Processing Tools]] ==<br />
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We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction. <br />
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<font color="red">'''New: '''</font> We have recently developed software for eddy current correction.<br />
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== [[Projects:NonRigidEPIRegistration|Non-Rigid EPI Registration]] ==<br />
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Our Objective is to identify optimal ITK method and parameter settings for non-rigid intrasubject registration of T2 EPI, the raw building block images of DTI, to T1 conventional images. Provide software devliverable. [[Projects:NonRigidEPIRegistration|More...]]<br />
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<font color="red">'''New: '''</font> Project Week Results: [[Engineering:Project:Non-rigid_EPI_registration|Jan 2006]]<br />
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== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==<br />
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In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]<br />
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<font color="red">'''New: '''</font><br />
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* ISBI 2007 submission<br />
* Currently working with cortical data using sulcal depth and evaluating on sulcal depth/cortical thickness data.<br />
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== [[Projects:DTIPopulationAnalysis|Population Analysis from Deformable Registration]] ==<br />
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Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics. This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. [[Projects:DTIPopulationAnalysis|More...]]<br />
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<font color="red">'''New: '''</font> Command line DTI tools available as part of UNC [http://www.ia.unc.edu/dev NeuroLib]<br />
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== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==<br />
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In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by<br />
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]<br />
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<font color="red">'''New: '''</font> R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.<br />
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== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==<br />
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We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image fidelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.<br />
[[Projects:RegistrationRegularization|More...]]<br />
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<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. In Proceedings of MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, 683-691, 2007. '''MICCAI Young Scientist Award.'''<br />
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== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==<br />
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In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.<br />
[[Projects:MultimodalAtlas|More...]]<br />
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<font color="red">'''New: '''</font> M.R. Sabuncu, M.E. Shenton, P. Golland. Joint Registration and Clustering of Images. In Proceedings of MICCAI 2007 Statistical Registration Workshop: Pair-wise and Group-wise Alignment and Atlas Formation, 47-54, 2007.<br />
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== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==<br />
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We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.<br />
[[Projects:GroupwiseRegistration|More...]]<br />
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<font color="red">'''New:'''</font> S.K. Balci, P. Golland, M.E. Shenton, W.M. Wells III. Free-Form B-spline Deformation Model for Groupwise Registration. In Proceedings of MICCAI 2007 Statistical Registration Workshop: Pair-wise and Group-wise Alignment and Atlas Formation, 23-30, 2007.<br />
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=== Morphometric Measures and Shape Analysis ===<br />
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== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==<br />
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We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]<br />
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<font color="red">'''New: '''</font> D. Nain, M. Styner, M. Niethammer, J. J. Levitt, M E Shenton, G Gerig, A. Bobick, A. Tannenbaum. Statistical Shape Analysis of Brain Structures using Spherical Wavelets. Accepted in The Fourth IEEE International Symposium on Biomedical Imaging (ISBI ’07) that will be held April 12-15, 2007 in Metro Washington DC, USA.<br />
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== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==<br />
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Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]<br />
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<font color="red">'''New:'''</font> Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.<br />
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== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==<br />
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In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]<br />
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<font color="red">'''New: '''</font> B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. Accepted to the IEEE Transactions on Image Processing. <br />
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P. Yu, B.T.T. Yeo, P.E. Grant, B. Fischl, P. Golland. Cortical Folding Development Study based on Over-Complete Spherical Wavelets. In Proceedings of MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2007.<br />
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== [[Projects:ShapeBasedLevelSetSegmentation|Shape Based Level Segmentation]] ==<br />
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This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. [[Projects:ShapeBasedLevelSetSegmentation|More...]]<br />
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== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==<br />
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This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]<br />
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<font color="red">'''New: '''</font> K.M. Pohl, J. Fisher, S. Bouix, M. Shenton, R. W. McCarley, W.E.L. Grimson, R. Kikinis, and W.M. Wells. Using the Logarithm of Odds to Define a Vector Space on Probabilistic Atlases. Medical Image Analysis,11(6), pp. 465-477, 2007. <b>Best Paper Award MICCAI 2006 </b><br />
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== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==<br />
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This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]<br />
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<font color="red">'''New: '''</font> J Cates, PT Fletcher, M Styner, M Shenton, R Whitaker, Shape modeling and analysis with entropy-based particle systems, IPMI 2007, pp. 333-345.<br />
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== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==<br />
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The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]<br />
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<font color="red">'''New: '''</font><br />
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* First version of Shape Analysis Toolset available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]) , this is to be added to the NAMIC toolkit.<br />
* Slicer 3 module for whole shape analysis pipeline in progress (based on BatchMake and distributed computing using Condor)<br />
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== [[Projects:ShapeAnalysisOfHippocampus|Shape Analysis of the Hippocampus]] ==<br />
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Our objective is to examine hippocampal shape in patients with schizophrenia and healthy controls. [[Projects:ShapeAnalysisOfHippocampus|More...]]<br />
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<font color="red">'''New: '''</font> Styner M, Lieberman JA, McClure RK, Weinberger DR, Jones DW, Gerig G.: Morphometric analysis of lateral ventricles in schizophrenia and healthy controls regarding genetic and disease-specific factors, Proc Natl Acad Sci USA. 2005 Mar 29;102(13):4872-7. Epub 2005 Mar 16.<br />
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== [[Projects:PopulationBasedCorrespondence|Population Based Correspondence]] ==<br />
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We are developing methodology to automatically find dense point correspondences between a collection of polygonal genus 0 meshes. The advantage of this method is independence from indivisual templates, as well as enhanced modeling properties. The method is based on minimizing a cost function that describes the goodness of correspondence. Apart from a cost function derived from the description length of the model, we also employ a cost function working with arbitrary local features. We extended the original methods to use surface curvature measurements, which are independent to differences of object aligment. [[Projects:PopulationBasedCorrespondence|More...]]<br />
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<font color="red">'''New: '''</font><br />
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* Software available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev website])<br />
* Evaluation on lateral ventricles, hippocampi, caudates, striatum, femural bone. Outperforms standard MDL on complex structures.<br />
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== [[Projects:CorticalSurfaceShapeAnalysisUsingSphericalWavelets|Spherical Wavelets]] ==<br />
Cortical Surface Shape Analysis Based on Spherical Wavelets. We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been proved to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant to rotation of the underlying surface parameterization. In this paper, we demonstrate the theoretical advantage of over-complete wavelets over bi-orthogonal wavelets and illustrate their utility on both synthetic and real data. In particular, we show that the over-complete spherical wavelet transform enjoys significant advantages for the analysis of cortical folding development in a newborn dataset. [[Projects:CorticalSurfaceShapeAnalysisUsingSphericalWavelets|More...]]<br />
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<font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007<br />
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== [[Projects:TopologyCorrectionNonSeparatingLoops|Topology Correction]] ==<br />
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Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically,we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator. [[Projects:TopologyCorrectionNonSeparatingLoops|More...]]<br />
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<font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007<br />
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== [[Projects:LocalStatisticalAnalysisViaPermutationTests|Local Statistical Analysis via Permutation Tests]] ==<br />
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We have further developed a set of statistical testing methods that allow the analysis of local shape differences using the Hotelling T 2 two sample metric. Permutatioin tests are employed for the computation of statistical p-values, both raw and corrected for multiple comparisons. Resulting significance maps are easily visualized. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. Ongoing research focuses on incorporating covariates such as clinical scores into the testing scheme. [[Projects:LocalStatisticalAnalysisViaPermutationTests|More...]]<br />
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<font color="red">'''New: '''</font><br />
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* Available as part of Shape Analysis Toolset in UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]).<br />
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== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==<br />
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We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]<br />
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== [[Projects:QDEC|QDEC: An easy to use GUI for group morphometry studies]] ==<br />
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Qdec is a application included in the Freesurfer software package intended to aid researchers in performing inter-subject / group averaging and inference on the morphometry data (cortical surface and volume) produced by the Freesurfer processing stream. The functionality in Qdec is also available as a processing module within Slicer3, and XNAT. [[Projects:QDEC|More...]]<br />
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See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]<br />
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|}</div>Vandymohanhttps://www.na-mic.org/w/index.php?title=File:GTTubSurfaceSeg-Img1.png&diff=36312File:GTTubSurfaceSeg-Img1.png2009-04-16T16:21:12Z<p>Vandymohan: uploaded a new version of "File:GTTubSurfaceSeg-Img1.png"</p>
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<div></div>Vandymohanhttps://www.na-mic.org/w/index.php?title=File:GTTubSurfaceSeg-Img1.png&diff=36311File:GTTubSurfaceSeg-Img1.png2009-04-16T16:18:33Z<p>Vandymohan: </p>
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<div></div>Vandymohan