DBE-Net: A Dual-Branch Boundary Enhancement Network for Pathological Image Segmentation

Pathological image segmentation provides support for the accurate assessment of lesion area by precisely segmenting various tissues and cellular structures in pathological images. Due to the unclear boundaries between targets and backgrounds, as well as the information loss during upsampling and downsampling operations, it remains a challenging task to identify boundary details, especially in differentiating between adjacent tissues, minor lesions, or clustered cell nuclei. In this paper, a Dual-branch Boundary Enhancement Network (DBE-Net) is proposed to improve the sensitivity of the model to the boundary. Firstly, the proposed method includes a main task and an auxiliary task. The main task focuses on segmenting the target object and the auxiliary task is dedicated to extracting boundary information. Secondly, a feature processing architecture is established which includes three modules: Feature Preservation (FP), Feature Fusion (FF), and Hybrid Attention Fusion (HAF) module. The FP module and the FF module are used to provide original information for the encoder and fuse information from every layer of the decoder. The HAF is introduced to replace the skip connections between the encoder and decoder. Finally, a boundary-dependent loss function is designed to simultaneously optimize both tasks for the dual-branch network. The proposed loss function enhances the dependence of the main task on the boundary information supplied by the auxiliary task. The proposed method has been validated on three datasets, including Glas, CoCaHis, and CoNSep dataset.