These improvements are in the context of automatic segmentations which are among the best found in the literature, exceeding agreement between experts. Also, in progress: Improved clustering (e.g., Gath-Geva) Deformation of the cluster regions Target image I is subdivided into object-relative regions, e.g. interior/exterior near boundary. Medial model m provides local correspondence between deformations of the same model. p(I|m) given by principal component analysis on quantile functions of corresponding regions’ histograms in training. Regional Appearance in Deformable Model Segmentation 2. Problem: Local Inhomogeneity 3. Three Exterior Regional Scales: 1. Image Match and m-reps 4. Segmentation Results 5. Discussion Goal: Improve deformable model segmentation through image appearance models that account for local regional inhomogeneity. Various tissue types tend to be at corresponding object-relative places over days. Leave-one-day-out experiment on each of 5 patients with each of the 3 appearance models Automatic segmentations compared a single expert rater using average surface distance and Dice volume overlap The local-geometric appearance model proved best overall, probably due to its more specific local intensity models. Paper: Paper: Proceedings, 2007 Information Processing in Medical Imaging (IPMI ’07) Acknowledgments: Acknowledgments: Conceptual, algorithmic and code contributions from Joshua Levy, Gregg Tracton and Graham Gash. Partially funded by NIH-NIBIB P01 EB Segmentation trends, volume overlap of automatic versus manual Cluster populations with cluster centers in black and as densities. PCA on the sets of interior and exterior RIQFs to obtain two RIQF models with Gaussian variability. PCA on each region separately, to obtain many RIQF models that together form object appearance. Local-clustered: single interior and several exterior regions Cluster on the pooled local-geometric RIQFs (see left) to obtain region-types. Partition the object boundary according to representative region-type. Regional intensity quantile functions (RIQFs) are amenable to Euclidean statistical methods like PCA. Intensity histograms RIQFs = Inverse CDFs Cluster center densities. Clusters in RIQF space. Bladder m-rep and surface with corresponding region highlighted Object 1Object 2 Large-scale object-exterior is informative, but is not from a single tissue mixture. Voxel-scale regions are too noisy, lack correspondence. Region-type partition for the bladder. Clustering yields biologically sensible regions. Bladder Prostate Global (red) versus local- geometric Local- geometric versus manual Local-geometric: single interior and many overlapping geometrically- defined exterior regions Global regions: sample the training image near the object boundary, compute the RIQFs for each case, interior and exterior. PCA on the region-type populations to obtain several RIQF models. Joshua V. Stough, Robert. E. Broadhurst, Stephen M. Pizer, Edward L. Chaney Medical Image Display & Analysis Group, University of North Carolina at Chapel Hill