1. Clustering on Local Appearance for Deformable Model Segmentation Joshua V. Stough, Robert E. Broadhurst, Stephen M. Pizer, Edward L. Chaney MIDAG, UNC-Chapel Hill ISBI 2007, SA-AM-OS2b April 14, 2007 MIDAG, UNC-Chapel Hill ISBI 2007, SA-AM-OS2b April 14, 2007

2. Deformable Model Segmentation Deformable Model Segmentation

3. Problem: Bladder and Prostate in CT Problem: Bladder and Prostate in CT ä Low contrast ä Variability across days. ä Low contrast ä Variability across days.

4. Clustering on Local Appearance for Deformable Model Segmentation ä Bayesian Deformable Model Segmentation ä Appearance / Image Match ä Clustering on Local Appearance ä Results on bladders and prostates in CT ä Conclusions ä Bayesian Deformable Model Segmentation ä Appearance / Image Match ä Clustering on Local Appearance ä Results on bladders and prostates in CT ä Conclusions

5. Deformable Model Segmentation (DMS) for Radiation Treatment Planning ä Segmentation is challenging and high cost for clinicians. ä Bayesian DMS ä Image match trained on expert segmentations ä Segmentation is challenging and high cost for clinicians. ä Bayesian DMS ä Image match trained on expert segmentations

6. Deformable Model and Locality Deformable Model and Locality ä M-rep provides: ä Boundary rep. with normals ä Correspondence ä Statistical deformation p(m) ä p(I | m), image match ä We consider appearance at local patches. ä M-rep provides: ä Boundary rep. with normals ä Correspondence ä Statistical deformation p(m) ä p(I | m), image match ä We consider appearance at local patches. Atom grid Implied Surface [Pizer et al., IJCV 55 (2) 2003] [Fletcher et al., TMI 23 (8) 2004]

7. Image Match p(I | m): Related Work Image Match p(I | m): Related Work ä Profile-based, voxel-scale correspondence ä AAM, [Cootes et al., CVIU 61 (1) 1995] ä AAM +, [Scott et al., IPMI 2003] ä Intensity Profile Clustering, [Stough et al., ISBI 2004] ä Region-based ä Intensity Ranges, [Zhu et al., PAMI 18 (9) 1996] ä Summary Statistics ä [Tsai et al., TMI 22 (2) 2003] ä [Chan et al., TIP 10 (2) 2001] ä Histogram Metrics ä [Rubner et al., CVIU 84 2001] ä [Freedman et al., TMI 24 (3) 2005] ä Statistics on Distributions, [Broadhurst et al., ISBI 2006] ä Profile-based, voxel-scale correspondence ä AAM, [Cootes et al., CVIU 61 (1) 1995] ä AAM +, [Scott et al., IPMI 2003] ä Intensity Profile Clustering, [Stough et al., ISBI 2004] ä Region-based ä Intensity Ranges, [Zhu et al., PAMI 18 (9) 1996] ä Summary Statistics ä [Tsai et al., TMI 22 (2) 2003] ä [Chan et al., TIP 10 (2) 2001] ä Histogram Metrics ä [Rubner et al., CVIU 84 2001] ä [Freedman et al., TMI 24 (3) 2005] ä Statistics on Distributions, [Broadhurst et al., ISBI 2006]

8. Appearance: Regional Intensity Quantile Functions (RIQFs) ä Inverse cumulative distribution function. ä Suited to PCA. ä Regional – local object-relative image extent. ä Example: probability density and quantile function. ä Inverse cumulative distribution function. ä Suited to PCA. ä Regional – local object-relative image extent. ä Example: probability density and quantile function. [Levina, UC-Berkeley 2002] [Broadhurst et al., ISBI 2006]

9. Question: Which Image Match Produces Best Segmentations? Global regions Versus geometrically defined local regions Versus regions defined by RIQF clusters. Global regions Versus geometrically defined local regions Versus regions defined by RIQF clusters.

10. Determine Region Types Determine Region Types ä Pool RIQFs over all regions and training images. ä Fuzzy C-Means Clustering ä Example: C = 2 on bladder exterior. ä Pool RIQFs over all regions and training images. ä Fuzzy C-Means Clustering ä Example: C = 2 on bladder exterior. [Bezdec 1981]

11. Partition the Boundary by Cluster Type Eig. 1 Z-score Eig. 2 Z-score ä For each patch, choose most popular cluster. ä PCA on cluster populations. ä For each patch, choose most popular cluster. ä PCA on cluster populations.

12. Bladder Partition for C = 2 Bladder Partition for C = 2 ä Confirming evidence ä Bladder: mostly fat with prostate and bone ä Prostate: dense tissue with bone and bladder, some fat ä Confirming evidence ä Bladder: mostly fat with prostate and bone ä Prostate: dense tissue with bone and bladder, some fat

13. Experimental Setup Experimental Setup 1. Determine local RIQF-types in training data 2. Construct Gaussian models on each type 3. Build a template of optimal types ä 5 patient image sets, ~16 images per patient. ä UNC RadOnc and William Beaumont, Michigan. ä 512  512 0.98  0.98  3 mm ä 5 patient image sets, ~16 images per patient. ä UNC RadOnc and William Beaumont, Michigan. ä 512  512 0.98  0.98  3 mm

14. Results Summary, Global v Clustered Results Summary, Global v Clustered Bladder GlobDSCClustDSCGlobASDClustASD 191.0%92.0 1.43mm 1.50 293.593.61.231.15 390.991.31.581.48 493.793.91.161.14 589.789.92.131.98 ProstateGlobDSCClustDSCGlobASDClustASD190.2%91.80.98mm0.82 292.092.31.341.26 392.392.00.950.94 493.994.20.970.93 591.390.01.591.78 RIQF clustered image match compares favorably with global.

15. Future Directions: ä Improved clustering ä Modeling mixtures ä Region shifting Future Directions: ä Improved clustering ä Modeling mixtures ä Region shifting Conclusions: ä Local-clustered regions lead to improved segmentations ä Already approaching expert quality, exceeding agreement between experts. Conclusions: ä Local-clustered regions lead to improved segmentations ä Already approaching expert quality, exceeding agreement between experts.

16. Prostate Arm

17. BowelBowel

18. A walk in the projected space of local intensity distributions

19. Cluster statistics are not specific enough. ä Evidence for modeling each patch separately.