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Collaborative Advances in Medical Image Segmentation and Volumetric Tractography

This document details the collaborative efforts of five investigators from various prestigious institutions. They engage in regular interactions with Data Base Partners (DBPs), including formal visits and teleconferences, to advance projects in medical image analysis. Key research areas include volumetric tractography, longitudinal shape analysis, image segmentation, and robust image registration. The emphasis is on developing efficient algorithms and frameworks for robust shape correspondences, statistical analysis of shapes, and nonparametric modeling, aimed at improving outcomes in medical imaging applications.

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Collaborative Advances in Medical Image Segmentation and Volumetric Tractography

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  1. Algorithms Core (C1a) • Five investigators: • A. Tannenbaum (BU), P. Golland (MIT), M. Styner (UNC), G. Gerig (Utah), R. Whitaker (Utah) • Regular interactions with DBPs • In total: ~5 formal in-person visits, ~10 tcons, lots of informal meetings and correspondence • Engineering Core Interactions • PIs, students, staff at Project Weeks • Ad hoc t-con participation

  2. Algorithms Core (C1a) • Core 1 Interactions • Project weeks • Monthly t-cons • Annual retreat (Oct. 2011, Mass; Aug. 2012, Utah) • Includes students, fellows, guests • Conference venues • IPMI 2011, MICCAI 2011

  3. NAMIC Activities – Utah HD AF HNC TBI • Image/Shape Analysis • Volumetric tractography and DTI atlases • Robust correspondences for shape • Longitudinal shape analysis • Segmentation • Globally optimal surface estimation • Fast, feature/shape-based image lookup • Registration • Robust metrics for image match • Nonsmoothregularizers

  4. Volumetric Tractography Tract – Statistics Tract – Patient Atlas – Tensors Atlas – FA • DTI Atlases to define ROIs • Goodlet et al. • Volumetric tractography for white matter regions • Fletcher et al. • Group analysis on volumetric regions

  5. Robust Shape Correspondences LV – Ischemic vs normal controls • Datar et al. 2011 • Statistics of points and normals • Geodesic distances for point-to-point interactions

  6. Longitudinal Shape Parameterizationwith T. Fletcher, M. Datar Left atrium trend – before and after ablation • Mixed effects model • Hierarchical–properly accounts for staggered/missing data in individuals

  7. Applications: Iowa/HD

  8. Segmentation • Graph-based image segmentation • Represent surfaces a min-cut in properly ordered graph • Liu et al. 2009 • Challenges • Objective functions that capture features, smoothness, coupled surfaces • Generalizations to 3D shapes

  9. Segmentation: Afib/Utah

  10. Fast Nearest Neighbor Lookup • Problem: from a large database of images, most similar images combine to form best segmentation • Label voting or nonparametric modeling paradigm • Especially important for heterogeneous data • Head&neck, cardiac • How to find similar shapes? • Deformation (slow) • Feature-based query (fast) • Zhu et. al, MICCAI 2010

  11. Feature-Based Lookup • Strategy • Detect features and compare hierarchically • Pyramid matching (Grauman 2006) • Brain MRI for label-based segmentation

  12. Fast Image Lookup: HNC parotid gland 9 images ground truth 3 best images

  13. Robust Image Registration • Applications • Correspondence or coordinate system for comparing different individuals or times • E.g. recovery from TBI, Afib before and after ablation • Segmentation from atlases or label voting • E.g. head and neck, endocardium from DCE • Challenges • Nonsmooth transformations • Singularities, tearing, sliding • Outliers/mismatches

  14. Registration Formulation

  15. Registration: TBI/UCLA TV Regularization Elastic Control/Template TBI Patient TV Regularization Elastic

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