GCP-Net: A Gating Context-Aware Pooling Network for Cervical Cell Nuclei Segmentation

Abstract

Accurate segmentation of cervical nuclei is an essential step in the early diagnosis of cervical cancer. Still, there are few studies on the segmentation of clustered nuclei in clusters of cells. Because of the complexities of high cell overlap, blurred nuclei boundaries, and clustered cells, the accurate segmentation of clustered nuclei remains a pressing challenge. In this paper, we purposefully propose a GCP-Net deep learning network to handle the challenging cervical cluster cell images. The proposed U-Net-based GCP-Net consists of a pretrained ResNet-34 model as encoder, a Gating Context-aware Pooling (GCP) module, and a modified decoder. The GCP module is the primary building block of the network to improve the quality of feature learning. It allows the GCP-Net to refine details of feature maps leveraging multiscale context gating and Global Context Attention for the spatial and texture dependencies. The decoder block including Global Context Attention- (GCA-) Residual Block helps build long-range dependencies and global context interaction in the decoder to refine the predicted masks. We conducted extensive comparative experiments with seven existing models on our ClusteredCell dataset and three typical medical image datasets, respectively. The experimental results showed that the GCP-Net obtained promising results on three evaluation metrics AJI, Dice, and PQ, demonstrating the superiorities and generalizability of our GCP-Net for automatic medical image segmentation in comparison with some SOAT baselines.