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NA-MIC Highlights: A Core 1 Perspective

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  1. NA-MIC Highlights:A Core 1 Perspective Ross Whitaker University of Utah National Alliance for Biomedical Image Computing

  2. Algorithms Productivity Things we have hardly thought about Clinical/ Biomedical Science Applied Methodology Validation/Evaluation New methods Things we have not yet published AJNR NeuroImage ICCV, PAMI MICCAI, MedIA, TMI Publications

  3. Algorithms Productivity Adopted by unaffiliated groups One-off prototypes Proof of concept Reusable (by friends) Software and tools

  4. Algorithms Productivity Clinical Practice The real goal

  5. Diffusion MRI in Schizophrenia Lee et al, “ Increased diffusivity in superior temporal gyrus in patients with schizophrenia: a Diffusion Tensor Imaging study”, Schiz. Res. 2009. Tissue classification and hand segmentation of STG Group differences and correlations with DTI measures

  6. DTI in Neurodevelopment Goodlett et. al, “Group analysis of DTI fi ber tract statistics with application to neurodevelopment”, Neuroimage, 2009.

  7. Longitudinal Studies of DTI Gouttard et. al, “Constrained Data Decomposition and Regression for Analyzing Healthy Aging from Fiber Tract Diffusion Properties”, MICCAI, 2009. Atlas based alignment and tract identification Localized statistics on longitudinal models

  8. Atlases and Segmentation for Scientific Studies Leemput et al., “Automated Segmentation of Hippocampal Subfields From Ultra-High Resolution In Vivo MRI”, Hippocampus, 2009. Bayesian Image analysis for parcellation of the hippocampus

  9. New Technologies for Atlases/Segmentation Riklin Raviv et al., “Joint Segmentation of Image Ensembles via Latent Atlases”, MICCAI 2009 Gerber et al., “On The Manifold Structure of the Space of Brain Images”, MICCAI 2009 Bootstrapping atlas with very little prior data Discovering/utilizing underlying parameters of large image databases

  10. New Technologies for Segmentation Karasev et al., “Conformal Geometric Flows for Surface Segmentation”, 2010 Prastawa et al., “Stastical analysis and segmentation with pathology”, 2010 Applications of statistical atlases with allowances for outliers Region specification by geometric flows on surfaces

  11. Shape Analysis in Schizophrenia Levitt et al., “Shape abnormalities of caudate nucleus in schizotypal personality disorder”, Schiz. Res., 2009. Global and local caudate shape abnormalities in male and female SPD

  12. Correspondence and Shape: Multimodal, Cortex Oguz et al., “Cortical correspondence with probabilistic fiberconnectivity”, IPMI, 2009. Combine shape and connectivity for group correspondence More consistent alignment of cortex relative to state of the art

  13. Shape and Regression/Development Datar et al., “Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging”, MICCAI, 2009. Correspondence incorporate an underlying developmental model

  14. Where We Are Headed Clinical Practice New Ideas Biomedical/Clinical Science

  15. Stay Tuned! • Investigators: • P. Golland – MIT • A. Tannenbaum – Georgia Tech • M. Stynder – UNC • G. Gerig – Utah