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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
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
volume tractography
Volume Tractography
  • DTI Atlases to define ROIs
    • Goodletet al.
  • Volumetric tractography for white matter regions
    • Fletcher et al.
  • Group analysis on volumetric regions
robust shape correspondences
Robust Shape Correspondences
  • Datar et al. 2011
    • Statistics of points and normals
    • Geodesic distances for point-to-point interactions
longitudinal shape parameterization with t fletcher m datar
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
segmentation
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
fast nearest neighbor lookup
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)
feature based lookup
Feature-Based Lookup
  • Strategy
    • Detect features and compare hierarchically
      • Pyramid matching (Grauman 2006)
robust image registration
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
registration formulation
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
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