Problem

1. Heterogeneous Conditional Random Field (HCRF): Realizing Joint Detection and Segmentation of Cell Regions in Microscopic Images Jiyan Pan1, Takeo Kanade1, and Mei Chen2 1Carnegie Mellon University, 2Intel Labs Pittsburgh 1 {jiyanpan, tk}@cs.cmu.edu, 2mei.chen@intel.com Results Conclusion • Cell Type • bovine aortic endothelial cells • C2C12 muscle stem cells • For each cell type • 10 images for training • 10 images for testing • Compare HCRF with • separate detection and segmentation • conventional CRF Detect and segment out individual cells in a dense population in microscopic images • The state set is {0,1,…,N}, N is the total number of interest points • The resulting model isunidentifiable • Maximum posterior probability shared by several states • CRF cannot select the correct state assign- ment combination SIFT? No stable spatial structures Sliding window? Cell shapes highly irregular N-cuts? Not discriminative Needs total # of cells Bovine C2C12 Input • Proposed approach: • Extract interest points and features • Classify points into cell or background (detection) • Group points within the same cell (segmentation) • Extend points to regions Conventional CRF Problem Approach Separate Detection/Segmentation Heterogeneous CRF (HCRF) Joint Detection/Segmentation by CRF Separate HCRF Before MAP inference • Give nodes an arbitrary ordering • Restricted propagation rule • Each node propagates its node index in turn • A node neither accepts nor passes on any state greater than its node index CRF • Two critical parameters to tune • Cannot recover from detection errors • No mutual enhancement between detection and segmentation Remaining Unidentifiability Bovine C2C12 After MAP inference Non-maxima suppression rule If a node’s maximum posterior probability is shared by several states, it takes the largest state • Joint detection and segmentation outperforms sep- arate detection and segmentation • Conventional CRF cannot achieve joint detection and segmentation due to unidentifiability • HCRF resolves unidentifiability by heterogeneous st- ate sets and non-maxima suppression rule • HCRF is provably complete, irreducible, unique, and sound