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:

Affine

Splines

Diffeomorphisms


Spline model
Spline model

Define

Identify

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


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