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arXiv 1908.066232019-08-19cs.CV

IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation

The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology, Co., Ltd.（视见医疗科技有限公司）；Imperial College London（帝国理工学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）

Yanning Zhou, Hao Chen, Jiaqi Xu, Qi Dou, Pheng-Ann Heng

备注：: Accepted by the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention

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Cell instance segmentation in Pap smear image remains challenging due to the wide existence of occlusion among translucent cytoplasm in cell clumps. Conventional methods heavily rely on accurate nuclei detection results and are easily disturbed by miscellaneous objects. In this paper, we propose a novel Instance Relation Network (IRNet) for robust overlapping cell segmentation by exploring instance relation interaction. Specifically, we propose the Instance Relation Module to construct the cell association matrix for transferring information among individual cell-instance features. With the collaboration of different instances, the augmented features gain benefits from contextual information and improve semantic consistency. Meanwhile, we proposed a sparsity constrained Duplicate Removal Module to eliminate the misalignment between classification and localization accuracy for candidates selection. The largest cervical Pap smear (CPS) dataset with more than 8000 cell annotations in Pap smear image was constructed for comprehensive evaluation. Our method outperforms other methods by a large margin, demonstrating the effectiveness of exploring instance relation.

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