# IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation

## TL;DR

This paper introduces IRNet, a novel instance relation network that enhances overlapping cervical cell segmentation by leveraging instance interactions and a duplicate removal module, significantly improving accuracy on a large Pap smear dataset.

## Contribution

The paper presents a new IRNet architecture with an instance relation module and a sparsity constrained duplicate removal module for improved cell segmentation.

## Key findings

- IRNet outperforms existing methods by a large margin.
- The method effectively handles occlusion and overlapping in cell images.
- Constructed a large CPS dataset with over 8000 annotations.

## Abstract

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.