Heterogeneous Conditional Random Field: Realizing Joint Detection and Segmentation of Cell Regions in Microscopic Images

Abstract

Detecting and segmenting cell regions in microscopic images is a challenging task, because cells typically do not have rich features, and their shapes and appearances are highly irregular and flexible. Furthermore, cells often form clusters, rendering the existing joint detection and segmentation algorithms unable to segment out individual cells. We address these difficulties by proposing a Heterogeneous Conditional Random Field (HCRF), in which different nodes have different state sets. The state sets are designed in such a way that the resulting HCRF model could encode all possible detection/segmentation cases while keeping its identifiability and compactness. Attributed to the provably optimal design of the state sets, the proposed model successfully realizes joint detection and segmentation of the cell regions into individual cells whether the cells are separate or touch one another. Experiments on two different types of cell images show that the HCRF outperforms several recently proposed methods.