IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation
This addresses the challenge of cell instance segmentation in medical imaging for overlapping cells, which is incremental as it builds on existing methods with novel modules.
The paper tackles the problem of overlapping cervical cell segmentation in Pap smear images by proposing an Instance Relation Network (IRNet) that uses instance relation interaction, resulting in outperforming other methods by a large margin on a dataset with over 8000 cell annotations.
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.