1. MIDAG@UNC Clustering on Image Boundary Regions for Deformable Model Segmentation Joshua Stough, Stephen M. Pizer, Edward L. Chaney, Manjari Rao, Gregg Tracton MIDAG Project Leaders: Chaney, Pizer 2004 UNC Radiology Research Symposium April 24, 2004 Joshua Stough, Stephen M. Pizer, Edward L. Chaney, Manjari Rao, Gregg Tracton MIDAG Project Leaders: Chaney, Pizer 2004 UNC Radiology Research Symposium April 24, 2004

2. MIDAG@UNC Automatic Segmentation of the Kidney in CT ä Deformable Model Segmentation ä Geometry ä Image Match, Template ä Deformable Model Segmentation ä Geometry ä Image Match, Template Goal: Robust image template capturing the dominant intensity pattern at each object boundary position in a set of training images

3. MIDAG@UNC M-rep Deformable Model ä Provides ä Boundary representation ä Correspondence ä Profiles—cross boundary image intensity samples ä Provides ä Boundary representation ä Correspondence ä Profiles—cross boundary image intensity samples Profile sample positions Atom grid Implied Surface

4. MIDAG@UNC Problem: Boundary Region Variability ä Relative object position affects intensity pattern. ä Local profile types. ä grey-to-light, grey-to-dark, notch ä Relative object position affects intensity pattern. ä Local profile types. ä grey-to-light, grey-to-dark, notch Axial CT slice Coronal CT slice

5. MIDAG@UNC Profiles in the Kidney Boundary Region ä How can we describe the dominant intensity pattern in the template? Axial CT slice Coronal CT slice IN OUT

6. MIDAG@UNC Our Approach: Build a Locally Varying Template from a Small Set of Profile Types ä Train the Profile Types Iteration 0 12 Cluster centers after iterations Training profiles grouped by cluster, with ± 2 .

7. MIDAG@UNC Our Approach: Build a Locally Varying Template ä Build the Template from the Profile Types ä For each point, pick the most representative type. ä Build the Template from the Profile Types ä For each point, pick the most representative type. Inside Outside Family of profiles Profile types Scores ä Green wins for this point.

8. MIDAG@UNC Resulting Template Best Describes Dominant Intensity Pattern in the Boundary Region Inside Outside Left kidneyRight kidney

9. MIDAG@UNC Experiment on Kidney Segmentation from CT ä Template training based on 52 abdominal CT scans; testing on 12 different scans ä Acquired on a Somatom 4+, UNC Radiation Oncology ä Windowed for consistent kidney intensity across cases ä Left and right kidney trained and tested separately ä Template training based on 52 abdominal CT scans; testing on 12 different scans ä Acquired on a Somatom 4+, UNC Radiation Oncology ä Windowed for consistent kidney intensity across cases ä Left and right kidney trained and tested separately Our method determines local cross-boundary image profile types in the space of training data, then builds a template of optimal types.

10. MIDAG@UNC Results on Kidney Segmentation from CT ä 12 left, 12 right, 2 expert segmentations per kidney ä Versus constant Gaussian Derivative: light-to-dark profile previously used ä improvement in 65% of the cases (31 of 48) using average surface distance ä average increase in the volume overlap of automatic and expert of 1.3% ä Qualitative advantage: required less manual parameter tweaking ä 12 left, 12 right, 2 expert segmentations per kidney ä Versus constant Gaussian Derivative: light-to-dark profile previously used ä improvement in 65% of the cases (31 of 48) using average surface distance ä average increase in the volume overlap of automatic and expert of 1.3% ä Qualitative advantage: required less manual parameter tweaking

11. MIDAG@UNC Future Research, Current Additions ä Use probability distributions on profiles from the training images ä Consider inside and outside profiles separately. ä Mixture model to account for some variability. ä Application to multi- object model. ä Use probability distributions on profiles from the training images ä Consider inside and outside profiles separately. ä Mixture model to account for some variability. ä Application to multi- object model.

12. MIDAG@UNC That’s It! Thank You. midag.cs.unc.edu Stough, Joshua, Stephen M. Pizer, Edward L. Chaney, Manjari Rao, Clustering on Image Boundary Regions for Deformable Model Segmentation, Proceedings 2004 IEEE International Symposium on Biomedical Imaging, ISBI ’04. ieeexplore.ieee.org Manjari I. Rao, Analysis of a Locally Varying Intensity Template for Segmentation of Kidneys in CT Images, Masters Thesis, UNC Biomedical Engineering, 2003 (Advisor Edward L. Chaney, PhD, Dept. Radiation Oncology)