detection of anatomical landmarks
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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
Georgetown University Medical Center

Friday October 6, 2006

Detection of Anatomical Landmarks

Bruno Jedynak

Camille Izard

anatomical landmarks
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
automatic landmarking
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
automatic landmarking example
Automatic landmarking Example:
  • Given: 38 images expertly landmarked. K landmarks per image
  • Goal: landmark new images
  • Mean error per new image

Or expert evaluation

stochastic modeling
Stochastic modeling
  • Build a likelihood function:
  • Learn:
  • For each new image, compute:
template matching paradigm
Template matching paradigm

Identify landmarks with a deformation of the 3d space.

Examples of deformations:




spline model
Spline model



Such that

forward model
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, …)

estimating the tissue probability map
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
automatic landmarking of a new image
Automatic landmarking of a new image
  • Learn the photometry parameters
  • Use gradient ascent to maximize
current work
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