Interactive Segmentation for Pathological Images with Similarity-based Propagation

Jiawei Wang, Jinhua Liu, Lin Chen, Guangshun Wei, Yuanfeng Zhou

Abstract: The auxiliary diagnosis based on pathological images often requires detecting exact nuclear information. In this paper, we propose an iteratively-refined interactive segmentation network named PSINet that allows users to guide the segmentation process of the model by drawing scribbles. PSINet can learn long-range dependencies among different cell nuclei, allowing it to correct other nuclei without direct feedback when the user only corrects the segmentation results on a few nuclei. Experimental results show that the proposed network outperforms state-of-the-art iteratively-refined interactive segmentation networks.