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

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 csf csf- gm gm gm- wm wmoutliers HoH00.040.900.0600

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

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