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

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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


Detection of anatomical landmarks

Landmarking the hippocampus from Brain MRI


Manual landmarking of the hippocampus

Manual landmarking of the Hippocampus


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:


Landmarks are points

Landmarks are points

Define


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, …)


Forward model1

Forward Model


Tissue probability map

Tissue Probability Map


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


Results

Results


Results1

Results


Results2

Results


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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