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NAMIC Activities – Utah

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

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NAMIC Activities – Utah

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  1. NAMIC Activities – Utah HD AF HNC TBI • Image/Shape Analysis • Volumetric tractographyand 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

  2. Volume Tractography • DTI Atlases to define ROIs • Goodletet al. • Volumetric tractography for white matter regions • Fletcher et al. • Group analysis on volumetric regions

  3. Robust Shape Correspondences • Datar et al. 2011 • Statistics of points and normals • Geodesic distances for point-to-point interactions

  4. 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

  5. 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

  6. Graph-Based Segmentation

  7. 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)

  8. Feature-Based Lookup • Strategy • Detect features and compare hierarchically • Pyramid matching (Grauman 2006)

  9. Robust Image Registration • Applications • Correspondence or coordinate system for comparing different individuals or times • E.g. longitudinal 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

  10. Registration Formulation • Strategy • Image match: apply robust versions of image metric (e.g. other norms) • Regularization: norms and operators that allow for nonsmooth (noninvertable) transformations • Issues • Well posedness • Optimization

  11. Registration Example

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