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  Back to [[NA-MIC_Internal_Collaborations|NA-MIC Internal Collaborations]]
 
  Back to [[NA-MIC_Internal_Collaborations|NA-MIC Internal Collaborations]]
 
__NOTOC__
 
__NOTOC__
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= Structural Image Analysis =
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=== Image Segmentation ===
 
=== Image Segmentation ===
  
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| style="width:15%" | [[Image:ProstateDiagram.png|200px]]
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| style="width:15%" | [[Image:MITHippocampalSubfieldSegmentation.png|200px]]
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== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==
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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> 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.
 +
|}
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=== Image Registration ===
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{| cellpadding="10" style="text-align:left;"
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| style="width:15%" | [[Image:Sulcaldepth.png|200px]]
 
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| style="width:85%" |
  
== [[ProjectWeek200706:BrachytherapyNeedlePositioningRobotIntegration|Brachytherapy Needle Positioning Robot Integration]] ==
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== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==
  
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...]]
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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...]]
  
<font color="red">'''New: '''</font> Meeting at JHU on July 17-19, 2007.
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<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:Fig67.png|200px]]
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| | [[Image:ICluster_templates.gif|200px]]
 
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== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==
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== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==
  
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]
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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.
 
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[[Projects:MultimodalAtlas|More...]]
<font color="red">'''New: '''</font> J. Melonakos, Y. Gao, and A. Tannenbaum. Tissue Tracking: Applications for Brain MRI Classification.  SPIE Medical Imaging, 2007.
 
  
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<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:Striatum1.png|200px]]
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| | [[Image:CoordinateChart.png|200px]]
 
| |
 
| |
  
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==
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== [[Projects:SphericalDemons|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, [[Projects:SphericalDemons|More...]]
 +
 
  
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...]]
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<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.
 +
 
 +
<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> Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007.
 
  
 
|-
 
|-
  
| | [[Image:Dlpfc1.jpg|200px]]
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| | [[Image:JointRegSeg.png|200px]]
 
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| |
  
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==
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== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==
  
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...]]
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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.
 
+
[[Projects:RegistrationRegularization|More...]]
<font color="red">'''New: '''</font> Al-Hakim, et al. A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter. SPIE MI 2006.
 
  
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<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:Gatech caudateBands.PNG|200px]]
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| | [[Image:Cbg-dtiatlas-tracts.png|200px]]
 
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| |
  
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==
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== [[Projects:DTIPopulationAnalysis|Population Analysis from Deformable Registration]] ==
  
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...]]
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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...]]
  
<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.
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<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.
 +
|}
  
|-
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=== Morphometric Measures and Shape Analysis ===
  
| | [[Image:Stochastic-snake.png|200px]]
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{| cellpadding="10" style="text-align:left;"
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| style="width:15%" | [[Image:P1_small.png|200px|]]
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| style="width:85%" |
  
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==
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== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==
  
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]
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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...]]
  
<font color="red">'''New: '''</font> Currently under investigation.
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<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.  
  
 
|-
 
|-
  
| | [[Image:Gatech SlicerModel2.jpg|200px]]
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| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
 
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== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==
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== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==
  
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]
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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...]]
 +
 
 +
<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.
 +
 
 +
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)
  
 
|-
 
|-
  
 +
| | [[Image:UNCShape_CaudatePval_MICCAI06.png|200px]]
 
| |
 
| |
| |
 
  
== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==
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== [[Projects:LocalStatisticalAnalysisViaPermutationTests|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. [[Projects:LocalStatisticalAnalysisViaPermutationTests|More...]]
  
We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
+
<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
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]
 
  
T Tasdizen, S Awate, R Whitaker, A nonparametric, entropy-minimizing MRI tissue classification algorithm implementation using ITK, MICCAI 2005 Open-Source Workshop.
+
* 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:histo_matching.jpg|200px]]
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| | [[Image:Cbg-dtiatlas-tracts.png|200px]]
 
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== [[Projects:AutomaticFullBrainSegmentation|Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms]] ==
 
  
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...]]
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== [[Projects:DTIPopulationAnalysis|Group Analysis of DTI Fiber Tracts]] ==
  
<font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007
+
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> 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
Back to NA-MIC Internal Collaborations

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.