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.

本文提出了Instance Relation Network (IRNet)，通过探索实例关系互动来实现鲁棒性重叠细胞分割，其中 Instance Relation Module 构建细胞关联矩阵，协同不同实例通过上下文信息提高语义一致性，同时提出了一个稀疏约束的Duplicate Removal Module，消除了分类和定位精度之间的误差，该方法在Pap smear图像中的最大宫颈抹片(CPS)数据集上表现优异，证明了探索实例关系的有效性。

IRNet: 一种用于重叠宫颈细胞分割的实例关系网络