Difference between revisions of "NA-MIC Internal Collaborations:StructuralImageAnalysis"

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== [[ProjectWeek200706:BrachytherapyNeedlePositioningRobotIntegration|Brachytherapy Needle Positioning Robot Integration]] ==
+
== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==
  
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...]]
+
In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]
  
<font color="red">'''New: '''</font> Meeting at JHU on July 17-19, 2007.
+
<font color="red">'''New: '''</font> van Leemput K., Bakkour A., Benner T., Wiggins G., Wald L.L., Augustinack J., Dickerson B.C., Golland P., Fischl B. Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus. 2009 Jun;19(6):549-57.
 
 
|-
 
 
 
| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
 
| |
 
 
 
== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==
 
 
 
We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]
 
 
 
<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for identifying anatomical structures from medical imagery. September 2008.
 
 
 
 
 
|-
 
  
 +
van Leemput K. Encoding probabilistic brain atlases using Bayesian inference. IEEE Trans Med Imaging. 2009 Jun;28(6):822-37.
 
|}
 
|}
 
  
 
=== Image Registration ===
 
=== Image Registration ===
  
 
{| cellpadding="10" style="text-align:left;"
 
{| cellpadding="10" style="text-align:left;"
| style="width:15%" | [[Image:Results brain sag.JPG|200px]]
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| style="width:15%" | [[Image:Sulcaldepth.png|200px]]
 
| style="width:85%" |
 
| style="width:85%" |
 
== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==
 
 
The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]
 
 
<font color="red">'''New: '''</font> Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. SPIE Conference on Computational Imaging V, Jan 2007.
 
 
 
 
|-
 
 
 
| | [[Image:EPIT1Registration.png|height="200px"]]
 
| |
 
 
== [[Projects:NonRigidEPIRegistration|Non-Rigid EPI Registration]] ==
 
 
Our Objective is to identify optimal ITK method and parameter settings for non-rigid intrasubject registration of T2 EPI, the raw building block images of DTI, to T1 conventional images. Provide software devliverable. [[Projects:NonRigidEPIRegistration|More...]]
 
 
<font color="red">'''New: '''</font> Project Week Results: [[Engineering:Project:Non-rigid_EPI_registration|Jan 2006]]
 
 
|-
 
 
| | [[Image:Sulcaldepth.png|200px]]
 
| |
 
  
 
== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==
 
== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==
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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...]]
 
In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]
  
<font color="red">'''New: '''</font> Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.
+
<font color="red">'''New: '''</font> Oguz I., Niethammer M., Cates J., Whitaker R., Fletcher T., Vachet C., Styner M. Cortical Correspondence with Probabilistic Fiber Connectivity. Inf Process Med Imaging. 2009;21:651-63.  
 
 
|-
 
 
 
| | [[Image:Cbg-dtiatlas-tracts.png|200px]]
 
| |
 
 
 
== [[Projects:DTIPopulationAnalysis|Population Analysis from Deformable Registration]] ==
 
 
 
Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics.  This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. [[Projects:DTIPopulationAnalysis|More...]]
 
 
 
<font color="red">'''New: '''</font> Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142.
 
  
 
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[[Projects:MultimodalAtlas|More...]]
 
[[Projects:MultimodalAtlas|More...]]
  
<font color="red">'''NEW: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.
+
<font color="red">'''New: '''</font> Yeo B.T.T., Sabuncu M.R., Golland P., Fischl B. Task-Optimal Registration Cost Functions. Int Conf Med Image Comput Comput Assist Interv. 2009;12(Pt 1):1009-1017.
 
 
 
|-
 
|-
  
 +
| | [[Image:CoordinateChart.png|200px]]
 +
| |
  
|}
+
== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==
  
=== Morphometric Measures and Shape Analysis ===
+
We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]
  
{| cellpadding="10" style="text-align:left;"
 
| style="width:15%" | [[Image:Basis membership.png|200px]]
 
| style="width:85%" |
 
  
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==
+
<font color="red">'''New: '''</font> Yeo B.T.T., Sabuncu M.R., Vercauteren T., Ayache N., Fischl B., Golland P. Spherical Demons: Fast Surface Registration. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):745-753.
  
We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]
+
<font color="red">'''New: '''</font> Yeo B.T.T., Sabuncu M.R., Vercauteren T., Ayache N., Fischl B., Golland P. Spherical Demons: Fast Surface Registration. IEEE TMI, In Press.
  
<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.
 
  
 
|-
 
|-
  
| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
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| | [[Image:JointRegSeg.png|200px]]
 
| |
 
| |
  
== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==
+
== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==
  
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...]]
+
We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image fidelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
 
+
[[Projects:RegistrationRegularization|More...]]
<font color="red">'''New: '''</font>
 
 
 
* First version of Shape Analysis Toolset available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]) , this is to be added to the NAMIC toolkit.
 
* Slicer 3 module for whole shape analysis pipeline in progress (based on BatchMake and distributed computing using Condor)
 
  
 +
<font color="red">'''New:'''</font> Yeo B.T.T., Sabuncu M.R., Desikan R., Fischl B., Golland P. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Med Image Anal. 2008 Oct;12(5):603-15.
 +
 
 
|-
 
|-
  
| | [[Image:ShapeAnalysisOverviewStatsHippo05.jpg|200px]]
+
| | [[Image:Cbg-dtiatlas-tracts.png|200px]]
 
| |
 
| |
  
== [[Projects:ShapeAnalysisOfHippocampus|Shape Analysis of the Hippocampus]] ==
+
== [[Projects:DTIPopulationAnalysis|Population Analysis from Deformable Registration]] ==
  
Our objective is to examine hippocampal shape in patients with schizophrenia and healthy controls. [[Projects:ShapeAnalysisOfHippocampus|More...]]
+
Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics.  This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. [[Projects:DTIPopulationAnalysis|More...]]
  
<font color="red">'''New: '''</font> 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.
+
<font color="red">'''New: '''</font> Goodlett C., Fletcher P.T., Gilmore J.H., Gerig G. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. Neuroimage. 2009 Mar;45(1 Suppl):S133-42.
 +
|}
  
|-
+
=== Morphometric Measures and Shape Analysis ===
  
| | [[Image:UNCShape_ShapeCorrespondence.png|200px]]
+
{| cellpadding="10" style="text-align:left;"
| |
+
| style="width:15%" | [[Image:P1_small.png|200px|]]
 +
| style="width:85%" |
  
== [[Projects:PopulationBasedCorrespondence|Population Based Correspondence]] ==
+
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==
  
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...]]
+
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...]]
  
<font color="red">'''New: '''</font>
+
<font color="red">'''New: '''</font> Malcolm J.G., Rathi Y., Shenton M.E., Tannenbaum A. Label Space: A Coupled Multi-shape Representation. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 2):416-424.  
 
 
* Software available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev website])
 
* Evaluation on lateral ventricles, hippocampi, caudates, striatum, femural bone. Outperforms standard MDL on complex structures.
 
  
 
|-
 
|-
  
| | [[Image:overcomplete_vs_biorthogonal_wavelets.jpg|200px]]
+
| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
 
| |
 
| |
  
== [[Projects:CorticalSurfaceShapeAnalysisUsingSphericalWavelets|Spherical Wavelets]] ==
+
== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==
Cortical Surface Shape Analysis Based on Spherical Wavelets. We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been proved to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant to rotation of the underlying surface parameterization. In this paper, we demonstrate the theoretical advantage of over-complete wavelets over bi-orthogonal wavelets and illustrate their utility on both synthetic and real data. In particular, we show that the over-complete spherical wavelet transform enjoys significant advantages for the analysis of cortical folding development in a newborn dataset. [[Projects:CorticalSurfaceShapeAnalysisUsingSphericalWavelets|More...]]
 
  
<font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007
+
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...]]
 
 
|-
 
 
 
| | [[Image:separating_loops.jpg|200px]]
 
| |
 
 
 
== [[Projects:TopologyCorrectionNonSeparatingLoops|Topology Correction]] ==
 
  
Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically,we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator. [[Projects:TopologyCorrectionNonSeparatingLoops|More...]]
+
<font color="red">'''New: '''</font> Zhu H., Zhou H., Chen J., Li Y., Lieberman J., Styner M. Adjusted exponentially tilted likelihood with applications to brain morphology. Biometrics. 2009 Sep;65(3):919-27.
  
<font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007
+
Levitt J.J., Styner M., Niethammer M., Bouix S., Koo M., Voglmaier M.M., Dickey C., Niznikiewicz M.A., Kikinis R., McCarley R.W., Shenton M.E. Shape abnormalities of caudate nucleus in schizotypal personality disorder. Schizophr Res. 2009 May;110(1-3):127-139.
 +
* Shape Analysis Toolkit available as part of UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]).
 +
* Slicer 3 module for whole shape analysis pipeline in progress (data access via XNAT, processing via BatchMake and distributed computing using Condor)
  
 
|-
 
|-
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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...]]
 
We have further developed a set of statistical testing methods that allow the analysis of local shape differences using the Hotelling T 2 two sample metric. Permutatioin tests are employed for the computation of statistical p-values, both raw and corrected for multiple comparisons. Resulting significance maps are easily visualized. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. Ongoing research focuses on incorporating covariates such as clinical scores into the testing scheme. [[Projects:LocalStatisticalAnalysisViaPermutationTests|More...]]
  
<font color="red">'''New: '''</font>
+
<font color="red">'''New: '''</font> Paniagua B., Styner M., Macenko M., Pantazis D., Niethammer M. Local Shape Analysis using MANCOVA. Insight Journal, 2009 July-December, http://hdl.handle.net/10380/3124
  
* Available as part of Shape Analysis Toolset in UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]).
+
* Available as part of Shape Analysis Toolset in UNC Neurolib open source ([http://www.ia.unc.edu/dev/download/shapeAnalysis download]) with MANCOVA testing.
  
 
|-
 
|-
  
| | [[Image:GT-SulciOutlining1.jpg|200px]]
+
| | [[Image:Cbg-dtiatlas-tracts.png|200px]]
 
| |
 
| |
  
== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==
 
  
We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]
+
== [[Projects:DTIPopulationAnalysis|Group Analysis of DTI Fiber Tracts]] ==
  
|-
+
Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statisticsThis project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. [[Projects:DTIPopulationAnalysis|More...]]
 
 
| | [[Image:qdec.jpg|200px]]
 
| |
 
 
 
== [[Projects:QDEC|QDEC: An easy to use GUI for group morphometry studies]] ==
 
 
 
Qdec is a application included in the Freesurfer software package intended to aid researchers in performing inter-subject / group averaging and inference on the morphometry data (cortical surface and volume) produced by the Freesurfer processing streamThe functionality in Qdec is also available as a processing module within Slicer3, and XNAT. [[Projects:QDEC|More...]]
 
 
 
See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]
 
  
 +
<font color="red">'''New: '''</font> Goodlett C., Fletcher P.T., Gilmore J.H., Gerig G. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. Neuroimage. 2009 Mar;45(1 Suppl):S133-42.
 
|}
 
|}

Latest revision as of 13:08, 14 May 2010

Home < NA-MIC Internal Collaborations:StructuralImageAnalysis
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Structural Image Analysis

Image Segmentation

MITHippocampalSubfieldSegmentation.png

Bayesian Segmentation of MRI Images

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. More...

New: van Leemput K., Bakkour A., Benner T., Wiggins G., Wald L.L., Augustinack J., Dickerson B.C., Golland P., Fischl B. Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus. 2009 Jun;19(6):549-57.

van Leemput K. Encoding probabilistic brain atlases using Bayesian inference. IEEE Trans Med Imaging. 2009 Jun;28(6):822-37.

Image Registration

Sulcaldepth.png

Cortical Correspondence using Particle System

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

New: Oguz I., Niethammer M., Cates J., Whitaker R., Fletcher T., Vachet C., Styner M. Cortical Correspondence with Probabilistic Fiber Connectivity. Inf Process Med Imaging. 2009;21:651-63.

ICluster templates.gif

Multimodal Atlas

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

New: Yeo B.T.T., Sabuncu M.R., Golland P., Fischl B. Task-Optimal Registration Cost Functions. Int Conf Med Image Comput Comput Assist Interv. 2009;12(Pt 1):1009-1017.

CoordinateChart.png

Spherical Demons: Fast Surface Registration

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, More...


New: Yeo B.T.T., Sabuncu M.R., Vercauteren T., Ayache N., Fischl B., Golland P. Spherical Demons: Fast Surface Registration. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):745-753.

New: Yeo B.T.T., Sabuncu M.R., Vercauteren T., Ayache N., Fischl B., Golland P. Spherical Demons: Fast Surface Registration. IEEE TMI, In Press.


JointRegSeg.png

Optimal Atlas Regularization in Image Segmentation

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

New: Yeo B.T.T., Sabuncu M.R., Desikan R., Fischl B., Golland P. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Med Image Anal. 2008 Oct;12(5):603-15.

Cbg-dtiatlas-tracts.png

Population Analysis from Deformable Registration

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

New: Goodlett C., Fletcher P.T., Gilmore J.H., Gerig G. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. Neuroimage. 2009 Mar;45(1 Suppl):S133-42.

Morphometric Measures and Shape Analysis

P1 small.png

Label Space: A Coupled Multi-Shape Representation

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. More...

New: Malcolm J.G., Rathi Y., Shenton M.E., Tannenbaum A. Label Space: A Coupled Multi-shape Representation. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 2):416-424.

UNCShape OverviewAnalysis MICCAI06.gif

Shape Analysis Framework using SPHARM-PDM

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

New: Zhu H., Zhou H., Chen J., Li Y., Lieberman J., Styner M. Adjusted exponentially tilted likelihood with applications to brain morphology. Biometrics. 2009 Sep;65(3):919-27.

Levitt J.J., Styner M., Niethammer M., Bouix S., Koo M., Voglmaier M.M., Dickey C., Niznikiewicz M.A., Kikinis R., McCarley R.W., Shenton M.E. Shape abnormalities of caudate nucleus in schizotypal personality disorder. Schizophr Res. 2009 May;110(1-3):127-139.

  • Shape Analysis Toolkit available as part of UNC Neurolib open source (download).
  • Slicer 3 module for whole shape analysis pipeline in progress (data access via XNAT, processing via BatchMake and distributed computing using Condor)
UNCShape CaudatePval MICCAI06.png

Local Statistical Analysis via Permutation Tests

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

New: Paniagua B., Styner M., Macenko M., Pantazis D., Niethammer M. Local Shape Analysis using MANCOVA. Insight Journal, 2009 July-December, http://hdl.handle.net/10380/3124

  • Available as part of Shape Analysis Toolset in UNC Neurolib open source (download) with MANCOVA testing.
Cbg-dtiatlas-tracts.png


Group Analysis of DTI Fiber Tracts

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. More...

New: Goodlett C., Fletcher P.T., Gilmore J.H., Gerig G. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. Neuroimage. 2009 Mar;45(1 Suppl):S133-42.