Accurate Cell Segmentation in Digital Pathology Images via Attention Enforced Networks

TL;DR

This paper introduces AENet, an attention-enforced neural network that improves cell segmentation accuracy in digital pathology images by integrating local and global features, addressing stain variation and tissue diversity.

Contribution

The paper proposes a novel Attention Enforced Network with spatial and channel attention modules, a feature fusion branch, and a stain normalization method for enhanced cell segmentation.

Findings

Outperforms prior methods on MoNuSeg dataset

Effectively handles stain variation and tissue diversity

Reduces fragmented segmentation regions

Abstract

Automatic cell segmentation is an essential step in the pipeline of computer-aided diagnosis (CAD), such as the detection and grading of breast cancer. Accurate segmentation of cells can not only assist the pathologists to make a more precise diagnosis, but also save much time and labor. However, this task suffers from stain variation, cell inhomogeneous intensities, background clutters and cells from different tissues. To address these issues, we propose an Attention Enforced Network (AENet), which is built on spatial attention module and channel attention module, to integrate local features with global dependencies and weight effective channels adaptively. Besides, we introduce a feature fusion branch to bridge high-level and low-level features. Finally, the marker controlled watershed algorithm is applied to post-process the predicted segmentation maps for reducing the fragmented regions. In the test stage, we present an individual color normalization method to deal with the stain variation problem. We evaluate this model on the MoNuSeg dataset. The quantitative comparisons against several prior methods demonstrate the superiority of our approach.