1. National Alliance for Medical Image Computing http://na-mic.org Structure

2. National Alliance for Medical Image Computing http://na-mic.org Core 1: Overview Harvard Georgia TechUNC UtahMIT Segmentation Registration Foundational Methods Structural Features and Statistics Connective Features and Statistics 1. Shape and Atlas Based Segmentation 2. Statistical Shape Analysis 3, DTI Connectivity Analysis 1. Diffusion-based Registration 2.Group Effect Maps 3. Automatic Segmentation 1. DTI Processing 2. Surface Processing 3. PDE Implementations 1. Combined Statistical/PDE Methods 1. Quantitative DTI Analysis 2. Cross-Sectional Shape Analysis 2. Stochastic Flow Models

3. National Alliance for Medical Image Computing http://na-mic.org Core 1: Overview Computational tools for image analysis –Extract anatomical structures at many scales –Measure properties of extracted structures –Determine connectivity between extracted structures –Relate disease factors to measurements

4. National Alliance for Medical Image Computing http://na-mic.org Shape Analysis Developing pipeline protocols for population comparisons, jointly with UNC. Integrating discriminative analysis into the pipeline: –Shape-based classification

5. National Alliance for Medical Image Computing http://na-mic.org EM Segmentation with Non- Stationary Tissue Priors Integrating into Slicer M-Step E-Step Bias: Predict Error Image Correct Intensities MF-Step: Regularize Weights Estimate Tissue Probability Label Map

6. National Alliance for Medical Image Computing http://na-mic.org Already in NAMIC Software Shape prior for segmentation –Leventon 2001 –Added to ITK by others DTI visualization –O’Donnell (CSAIL), LMI (BWH) –In VTK-based 3D Slicer

7. National Alliance for Medical Image Computing http://na-mic.org Future work (6 months) Complete shape-based segmentation implementation –Insert into toolkit Shape based comparison and population analysis –Structural components –Tract components

8. National Alliance for Medical Image Computing http://na-mic.org Q-Ball Imaging in Slicer Estepar, Snyder, Kindlmann, Westin

9. National Alliance for Medical Image Computing http://na-mic.org Automatic Thalamus Segmentation LGN MGN VL MD VA VL MD VA CM Pu CM VL VA MD Ziyan, Tuch

10. National Alliance for Medical Image Computing http://na-mic.org 1. Make QBALL available Check QBALL code into Slicer and VTK. 2. Does nonlinear registration boost stats? Measure power benefit of ITK nonlinear registration for FA group comparisons. 3. Are group comparisons based on the full tensor more sensitive? Implement and measure sensitivity of tensor-based group comparison method. Future Work

11. National Alliance for Medical Image Computing http://na-mic.org Utah Core 1 Activities Differential Geometry for DTI analysis Descriptive statistics of DTI Hypothesis testing DTI Interpolation and filtering of DTI

12. National Alliance for Medical Image Computing http://na-mic.org Curved Tensor Geometry Natural geometry for tensor analysis Enforces positive eigenvalues Basis for statistics, interpolation, and processing Space of 2x2 tensors:

13. National Alliance for Medical Image Computing http://na-mic.org Descriptive Statistics Averages and Modes of Variation Preserves natural properties –Positive eigenvalues –Tensor Orientation –Tensor Size (determinant) Prototype implemented in ITK

14. National Alliance for Medical Image Computing http://na-mic.org Hypothesis Testing Tests differences in diffusion tensors from two groups Uses full six-dimensional information from tensors Prototype implemented in ITK Upcoming IPMI submission

15. National Alliance for Medical Image Computing http://na-mic.org Interpolation and Filtering Interpolation of tensors –Based on weighted averages in curved geometry Filtering –Anisotropic filtering based on curved geometry Implementation in progress

16. National Alliance for Medical Image Computing http://na-mic.org StatisticsProcessing Software Different tensor geometries can be defined Each package can swap in/out different geometries TensorGeometry LinearGeometry CurvedGeometry Other? DescriptiveStats TensorGeometry HypothesisTests TensorGeometry Interpolation TensorGeometry Filtering TensorGeometry

17. National Alliance for Medical Image Computing http://na-mic.org Future Work (6 months) Further develop tensor statistics— make publicly available Build prototypes of tensor filtering and interpolation Continue research into DTI hypothesis testing –Methods –Exploratory Experiments

18. National Alliance for Medical Image Computing http://na-mic.org UNC: Quantitative DTI Analysis Guido Gerig, Isabelle Corouge Students: Casey Goodlett and Clement Vachet

19. National Alliance for Medical Image Computing http://na-mic.org Conventional Analysis: ROI or voxel-based group tests after alignment Patient Control Quantitative DTI Analysis UNC NA-MIC Approach: Quantitative Analysis of Fiber Tracts DTI Tensor Statistics across/along fiber bundles Statistics of tensors Tracking/ clustering selectionFA FA along tract

20. National Alliance for Medical Image Computing http://na-mic.org Example: Fiber-tract Measurements Corouge, Isabelle, Gouttard, Sylvain and Gerig, Guido, "Towards a Shape Model of White Matter Fiber Bundles using Diffusion Tensor MRI", Proc. IEEE Computer Society, Int. Symp. on Biomedical Imaging, to appear April 2004 Gerig, Guido, Gouttard, Sylvain and Corouge, Isabelle, "Analysis of Brain White Matter via Fiber Tract Modeling", full paper IEEE Engineering in Medicine and Biology Society EMBS 2004, Sept. 2004 uncinate fasciculus FA along uncinate cingulum FA along cingulate Major fiber tracts

21. National Alliance for Medical Image Computing http://na-mic.org Processing Steps Tractography –Data structure for sets of attributed streamlines Clustering Parameterization Diffusion properties across/along bundles Graph/Text Output Statistical Analysis  Slicer (?)  ITK DTI Fiber Spatial Object data structure (J. Jomier)  Normalized Cuts (ITK)  B-splines (ITK)  NEW: DTI stats in nonlinear space (UTAH)  Display/Files  Biostatistics / ev. DTI hypothesis testing (UTAH)

22. National Alliance for Medical Image Computing http://na-mic.org Results FiberViewer Prototype (ITK) Clustering (various metrics) Parametrization FA/ADC/Eigen-value Statistics Uses SpatialObjects and SpatialObject-Viewer Used in two UNC clinical studies (neonates, autism) Validation: ISMRM’05

23. National Alliance for Medical Image Computing http://na-mic.org Next 6 months Methodology Development: –DTI tensor statistics: close collab. with UTAH –Deliver ITK tools for clustering/parameterization to Core 2 –Feasibility tests with tractography from Slicer –Deliver prototype platform to Core 2 to discuss integration into Slicer Clinical Study: DTI data from Core 3 –Check feasibility of tract-based analysis w.r.t. DTI resolution (isotropic voxels(?)), SNR –Apply procedure to measure properties of: Cingulate (replicate ROI findings) Uncinate fasciculus (replicate ROI findings) Other tracts of interest

24. National Alliance for Medical Image Computing http://na-mic.org UNC: Statistical Shape Analysis Martin Styner Students: Ipek Oguz and Christine Shun Xu

25. National Alliance for Medical Image Computing http://na-mic.org Shape Analysis Pipeline Clinical need: Localization of shape and volume changes 3D objects of spherical topology Input: Segmentation from models or binary images Modeling Steps: –Individual surface models Regularization Correspondence –Alignment via Procrustes & choice of scale –Skeletal description Structural subdivision Statistical analysis of models

26. National Alliance for Medical Image Computing http://na-mic.org Shape Analysis Pipeline Thickness maps –Distance to skeleton Local shape analysis –To template or template-free –Univariate Euclidean distance –Multivariate Hotelling T 2 distance –Raw p values, t/T 2 -maps, effect-size –Conservative correction for Type II error MIT discriminative analysis complements our shape analysis well Visualizations of steps for QC

27. National Alliance for Medical Image Computing http://na-mic.org Next 6 months NAMIC toolkit development –Standardization of IO & internal representation With MIT & Georgia Tech –Standardization of visualization tools –Automation of tools, transfer to standard Methodology development –Non-Euclidean shape metrics with permutation tests –Probabilistic structural subdivision method –3D visualization maps of statistical metrics Clinical: Shape analysis data from Core 3 –Feasibility of shape analysis on data from Core 3 –Caudate shape analysis on Brockton VA/Harvard data

28. National Alliance for Medical Image Computing http://na-mic.org Georgia Tech Ramsey Al-Hakim Steven Haker Delphine Nain Eric Pichon Allen Tannenbaum

29. National Alliance for Medical Image Computing http://na-mic.org Anisotropic active contours Add directionality

30. National Alliance for Medical Image Computing http://na-mic.org Curve minimization Calculus of variations –Start with initial curve –Deform to minimize energy –Steady state is locally optimum Dynamic programming –Choose seed point s –For any point t, determine globally optimal curve t  s Registration, Atlas-based segmentation Segmentation

31. National Alliance for Medical Image Computing http://na-mic.org Synthetic example (3D)

32. National Alliance for Medical Image Computing http://na-mic.org L2 Bases Functions Local Shape Analysis Our goal is to build more localized shape priors that can handle surfaces with high frequencies (high curvatures) and learn the local variations from the training set. We propose to compare different L2 bases. In particular, we would like to investigate the use of multiscale shape analysis and learn localized shape statistics from the data using bayesian statistics. The applications are shape prior for segmentation, registration and classification.

33. National Alliance for Medical Image Computing http://na-mic.org Example: Some Local Variations Finding local variations in Prostate data at different frequency levels and spatial locations Low Frequency High Frequency

34. National Alliance for Medical Image Computing http://na-mic.org Segmentation of Area 46 Using Fallon’s Rules-I Ramsey Al-Hakim BME Undergraduate

35. National Alliance for Medical Image Computing http://na-mic.org Segmentation of Area 46 Using Fallon’s Rules-II Ramsey Al-Hakim BME Undergraduate

36. National Alliance for Medical Image Computing http://na-mic.org Work in Next Six Months Choice of anisotropic conformal factors for DTI-tractography. Comparison of L2 bases for shape analysis (application to caudate). Making Fallon’s rules more automatic for segmentation.

37. National Alliance for Medical Image Computing http://na-mic.org Structure