W-Net: A Two-Stage Convolutional Network for Nucleus Detection in Histopathology Image

TL;DR

This paper introduces W-Net, a two-stage convolutional network that improves nucleus detection in histopathology images by splitting the task into binary and density mask predictions, enhancing accuracy and efficiency.

Contribution

The proposed W-Net architecture divides nucleus detection into two sub-tasks, reducing difficulty and improving performance over traditional U-Net based methods.

Findings

Enhanced nucleus detection accuracy.

Reduced manual annotation workload.

Improved segmentation performance.

Abstract

Pathological diagnosis is the gold standard for cancer diagnosis, but it is labor-intensive, in which tasks such as cell detection, classification, and counting are particularly prominent. A common solution for automating these tasks is using nucleus segmentation technology. However, it is hard to train a robust nucleus segmentation model, due to several challenging problems, the nucleus adhesion, stacking, and excessive fusion with the background. Recently, some researchers proposed a series of automatic nucleus segmentation methods based on point annotation, which can significant improve the model performance. Nevertheless, the point annotation needs to be marked by experienced pathologists. In order to take advantage of segmentation methods based on point annotation, further alleviate the manual workload, and make cancer diagnosis more efficient and accurate, it is necessary to develop an automatic nucleus detection algorithm, which can automatically and efficiently locate the position of the nucleus in the pathological image and extract valuable information for pathologists. In this paper, we propose a W-shaped network for automatic nucleus detection. Different from the traditional U-Net based method, mapping the original pathology image to the target mask directly, our proposed method split the detection task into two sub-tasks. The first sub-task maps the original pathology image to the binary mask, then the binary mask is mapped to the density mask in the second sub-task. After the task is split, the task's difficulty is significantly reduced, and the network's overall performance is improved.