Detection of anatomical landmarks
Sponsored Links
This presentation is the property of its rightful owner.
1 / 19

Detection of Anatomical Landmarks PowerPoint PPT Presentation

  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

Detection of Anatomical Landmarks

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

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

  • Used as is to analyze shapes

  • Used as control point for image segmentation/registration

Landmarking the hippocampus from Brain MRI

Manual landmarking of the Hippocampus

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:

  • Given: 38 images expertly landmarked. K landmarks per image

  • Goal: landmark new images

  • Mean error per new image

    Or expert evaluation

Stochastic modeling

  • Build a likelihood function:

  • Learn:

  • For each new image, compute:

Landmarks are points


Template matching paradigm

Identify landmarks with a deformation of the 3d space.

Examples of deformations:




Spline model



Such that

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 Model

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

  • Learn the photometry parameters

  • Use gradient ascent to maximize




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

  • Login