TopoSeg: Topology-Aware Nuclear Instance Segmentation

Abstract

Nuclear instance segmentation has been critical for pathology image analysis in medical science, e.g., cancer diagnosis. Current methods typically adopt pixel-wise optimization for nuclei boundary exploration, where rich structural information could be lost for subsequent quantitative morphology assessment. To address this issue, we develop a topology-aware segmentation approach, termed TopoSeg, which exploits topological structure information to keep the predictions rational, especially in common situations with densely touching and overlapping nucleus instances. Concretely, TopoSeg builds on a topology-aware module (TAM), which encodes dynamic changes of different topology structures within the three-class probability maps (inside, boundary, and background) of the nuclei to persistence barcodes and makes the topology-aware loss function. To efficiently focus on regions with high topological errors, we propose an adaptive topology-aware selection (ATS) strategy to enhance the topology-aware optimization procedure further. Experiments on three nuclear instance segmentation datasets justify the superiority of TopoSeg, which achieves state-of-the-art performance. The code is available at https://github.com/hhlisme/toposeg.