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

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=== Image Segmentation ===
 
=== Image Segmentation ===
  
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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...]]
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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> 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:Fig67.png|200px]]
 
| |
 
 
 
== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==
 
 
 
This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]
 
 
 
<font color="red">'''New: '''</font> J. Melonakos, Y. Gao, and A. Tannenbaum. Tissue Tracking: Applications for Brain MRI Classification.  SPIE Medical Imaging, 2007.
 
 
 
|-
 
 
 
| | [[Image:Striatum1.png|200px]]
 
| |
 
 
 
== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==
 
 
 
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...]]
 
 
 
<font color="red">'''New: '''</font> Al-Hakim, et al. Parcellation of the Striatum. SPIE MI 2007.
 
 
 
|-
 
 
 
| | [[Image:Dlpfc1.jpg|200px]]
 
| |
 
 
 
== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC 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...]]
 
 
 
<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.  
 
 
 
|-
 
 
 
| | [[Image:Gatech caudateBands.PNG|200px]]
 
| |
 
 
 
== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==
 
 
 
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...]]
 
 
 
<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.
 
 
 
|-
 
 
 
| | [[Image:Stochastic-snake.png|200px]]
 
| |
 
 
 
== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==
 
 
 
New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]
 
 
 
<font color="red">'''New: '''</font> Currently under investigation.
 
 
 
|-
 
 
 
| | [[Image:Gatech SlicerModel2.jpg|200px]]
 
| |
 
 
 
== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==
 
 
 
This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]
 
 
 
|-
 
 
 
| |
 
| |
 
 
 
== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==
 
 
 
We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
 
[[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.
 
 
 
|-
 
 
 
| | [[Image:histo_matching.jpg|200px]]
 
| |
 
 
 
== [[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...]]
 
 
 
<font color="red">'''New: '''</font> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007
 
  
 +
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 ===
 
=== Image Registration ===
  
{| cellpadding="10"
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{| 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]]
 
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== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==
+
== [[Projects:CorticalCorrespondenceWithParticleSystem|Cortical Correspondence using Particle System]] ==
  
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...]]
+
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> Tauseef ur Rehman, A. Tannenbaum. Multigrid Optimal Mass Transport for Image Registration and Morphing. SPIE Conference on Computational Imaging V, Jan 2007.
+
<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:DTIregistration200.png|200px]]
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| | [[Image:ICluster_templates.gif|200px]]
 
| |
 
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== [[Projects:DTIProcessingTools|Diffusion Tensor Image Processing Tools]] ==
+
== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==
  
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.
+
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.
 
+
[[Projects:MultimodalAtlas|More...]]
<font color="red">'''New: '''</font> We have recently developed software for eddy current correction.
 
  
 +
<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:EPIT1Registration.png|height="200px"]]
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| | [[Image:CoordinateChart.png|200px]]
 
| |
 
| |
  
== [[Projects:NonRigidEPIRegistration|Non-Rigid EPI Registration]] ==
+
== [[Projects:SphericalDemons|Spherical Demons: Fast Surface 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]] ==
 
 
 
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>
 
 
 
* ISBI 2007 submission
 
* Currently working with cortical data using sulcal depth and evaluating on sulcal depth/cortical thickness data.
 
 
 
|-
 
 
 
| | [[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> Command line DTI tools available as part of UNC [http://www.ia.unc.edu/dev NeuroLib]
 
  
|-
+
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...]]
  
| | [[Image:TruckInitialization.png|200px|]]
 
| |
 
  
== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==
+
<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.
  
In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
+
<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.
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...]]
 
  
<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.
 
  
 
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[[Projects:RegistrationRegularization|More...]]
 
[[Projects:RegistrationRegularization|More...]]
  
<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.'''
+
<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:ICluster_templates.gif|200px]]
+
| | [[Image:Cbg-dtiatlas-tracts.png|200px]]
 
| |
 
| |
  
== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==
+
== [[Projects:DTIPopulationAnalysis|Population Analysis from Deformable Registration]] ==
  
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.
+
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...]]
[[Projects:MultimodalAtlas|More...]]
 
 
 
<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.
 
 
 
|-
 
 
 
| | [[Image:GroupwiseSummary.PNG|200px]]
 
| |
 
 
 
== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==
 
 
 
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.
 
[[Projects:GroupwiseRegistration|More...]]
 
 
 
<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.
 
  
 +
<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 ===
 
=== Morphometric Measures and Shape Analysis ===
  
{| cellpadding="10"
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{| cellpadding="10" style="text-align:left;"
| style="width:15%" | [[Image:Basis membership.png|200px]]
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| style="width:15%" | [[Image:P1_small.png|200px|]]
 
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| style="width:85%" |
  
== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==
+
== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==
  
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...]]
+
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> 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.
+
<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:HippocampalShapeDifferences.gif|200px]]
 
| |
 
 
 
== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==
 
 
 
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...]]
 
 
 
<font color="red">'''New:'''</font> Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.
 
 
 
|-
 
 
 
| | [[Image:FoldingSpeedDetection.png|200px]]
 
| |
 
 
 
== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==
 
 
 
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...]]
 
 
 
<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.
 
 
 
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.
 
 
 
|-
 
 
 
| |
 
| |
 
 
 
== [[Projects:ShapeBasedLevelSetSegmentation|Shape Based Level Segmentation]] ==
 
 
 
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...]]
 
 
 
|-
 
 
 
| | [[Image:Progress_Registration_Segmentation_Shape.jpg|200px]]
 
| |
 
 
 
== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==
 
 
 
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...]]
 
 
 
<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>
 
 
 
|-
 
 
 
| | [[Image:Meanviews.png|200px]]
 
| |
 
 
 
== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==
 
 
 
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...]]
 
 
 
<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.
 
  
 
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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...]]
 
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>
+
<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.
 
 
* 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)
 
 
 
|-
 
 
 
| | [[Image:ShapeAnalysisOverviewStatsHippo05.jpg|200px]]
 
| |
 
 
 
== [[Projects:ShapeAnalysisOfHippocampus|Shape Analysis of the Hippocampus]] ==
 
 
 
Our objective is to examine hippocampal shape in patients with schizophrenia and healthy controls. [[Projects:ShapeAnalysisOfHippocampus|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.
 
 
 
|-
 
 
 
| | [[Image:UNCShape_ShapeCorrespondence.png|200px]]
 
| |
 
 
 
== [[Projects:PopulationBasedCorrespondence|Population Based Correspondence]] ==
 
  
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...]]
+
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]).
<font color="red">'''New: '''</font>
+
* Slicer 3 module for whole shape analysis pipeline in progress (data access via XNAT, processing via BatchMake and distributed computing using Condor)
 
 
* 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]]
 
| |
 
 
 
== [[Projects:CorticalSurfaceShapeAnalysisUsingSphericalWavelets|Spherical Wavelets]] ==
 
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
 
 
 
|-
 
 
 
| | [[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> IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007
 
  
 
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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.
  
 
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|-
  
| | [[Image:qdec.jpg|200px]]
+
| | [[Image:Cbg-dtiatlas-tracts.png|200px]]
 
| |
 
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== [[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 stream.  The functionality in Qdec is also available as a processing module within Slicer3, and XNAT. [[Projects:QDEC|More...]]
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== [[Projects:DTIPopulationAnalysis|Group Analysis of DTI Fiber Tracts]] ==
  
See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]
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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...]]
  
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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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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.