Edge-Aware Dual Branch Network for Nucleus Instance Segmentation

In mobile healthcare and remote diagnosis, nucleus segmentation is a critical step for pathological analysis, diagnosis, and classification, requiring real-time processing and high accuracy. However, variations in nucleus size, blurred contours, uneven staining, cell clustering, and overlapping cells hinder precise segmentation. Additionally, existing deep learning models often prioritize accuracy at the cost of increased complexity, making them unsuitable for resource-limited edge devices and real-world deployment. To address the aforementioned issues, we propose an edge-aware dual branch network for nucleus instance segmentation. The network simultaneously predicts target information and target contours. Within the network, we propose a context fusion block (CF-block) that effectively extracts and merges contextual information from the network. Additionally, we introduce a post-processing method that combines the target information and target contours to distinguish overlapping nuclei and generate an instance segmentation image. Extensive quantitative evaluations are conducted to assess the performance of our method. Experimental results demonstrate the superior performance of the proposed method compared to state-of-the-art approaches on the BNS, MoNuSeg, and CPM-17 datasets.