Nucleus image segmentation is a crucial step in the analysis, pathological diagnosis, and classification, which heavily relies on the quality of nucleus segmentation. However, the complexity of issues such as variations in nucleus size, blurred nucleus contours, uneven staining, cell clustering, and overlapping cells poses significant challenges. Current methods for nucleus segmentation primarily rely on nuclear morphology or contour-based approaches. Nuclear morphology-based methods exhibit limited generalization ability and struggle to effectively predict irregular-shaped nuclei, while contour-based extraction methods face challenges in accurately segmenting overlapping nuclei. To address the aforementioned issues, we propose a dual-branch network using hybrid attention based residual U-blocks for nucleus instance segmentation. The network simultaneously predicts target information and target contours. 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. Within the network, we propose a context fusion block (CF-block) that effectively extracts and merges contextual information from the network. 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, CoNSeg, and CPM-17 datasets.

翻译：核图像分割是分析、病理诊断和分类中的关键步骤，其准确性高度依赖于核分割质量。然而，核尺寸差异、核轮廓模糊、染色不均、细胞聚集及重叠细胞等复杂问题构成了显著挑战。当前核分割方法主要基于核形态学或轮廓策略。基于核形态学的方法泛化能力有限，难以有效预测不规则形态核；而基于轮廓提取的方法在分割重叠核时面临精准度不足的问题。针对上述问题，我们提出一种基于混合注意力残差U型块的双分支网络用于核实例分割。该网络同时预测目标信息与目标轮廓。此外，我们引入一种后处理技术，通过结合目标信息与目标轮廓来区分重叠核并生成实例分割图像。在网络内部，我们提出上下文融合模块（CF-block），可有效提取并融合网络中的上下文信息。通过大量定量评估验证了方法的性能。实验结果表明，在BNS、MoNuSeg、CoNSeg及CPM-17数据集上，本方法性能优于现有最先进技术。