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Detection of Anatomical Landmarks

Georgetown University Medical Center Friday October 6, 2006. Detection of Anatomical Landmarks. Bruno Jedynak Camille Izard. Anatomical Landmarks. Manually defined points in the anatomy ( geometric landmarks) !! Landmarker consistency, variability between exerts

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Detection of Anatomical Landmarks

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  1. Georgetown University Medical Center Friday October 6, 2006 Detection of Anatomical Landmarks Bruno Jedynak Camille Izard

  2. Anatomical Landmarks • Manually defined points in the anatomy ( geometric landmarks) • !! Landmarker consistency, variability between exerts • Used as is to analyze shapes • Used as control point for image segmentation/registration

  3. Landmarking the hippocampus from Brain MRI

  4. Manual landmarking of the Hippocampus

  5. Automatic landmarking • Given: a set of manually landmarked images • Goal: build a system that can landmark new images • The system must adapt to different kind, different number of landmarks

  6. Automatic landmarking Example: • Given: 38 images expertly landmarked. K landmarks per image • Goal: landmark new images • Mean error per new image Or expert evaluation

  7. Stochastic modeling • Build a likelihood function: • Learn: • For each new image, compute:

  8. Landmarks are points Define

  9. Template matching paradigm Identify landmarks with a deformation of the 3d space. Examples of deformations: Affine Splines Diffeomorphisms

  10. Spline model Define Identify Such that

  11. Forward model Brain MRI gray-values are modeled as a mixture of Gaussians distributions. There are 6 components in the mixture: CSF,GM, WM, CSF-GM, GM-WM, VeryWhite (Skull, blood vessels, …)

  12. Forward Model

  13. Tissue Probability Map

  14. Estimating the tissue probability map • Learn the photometry of each image • Register each image on the template • Use the E.M. algo. for mixture of Gaussians to estimate

  15. Automatic landmarking of a new image • Learn the photometry parameters • Use gradient ascent to maximize

  16. Results

  17. Results

  18. Results

  19. Current work • Estimating the std. dev. of the Kernels • Add control points to generate more complex deformations (K=1) • Test on schizophrenic and other brains

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