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：细胞核图像分割是分析、病理诊断及分类中的关键步骤，其效果在很大程度上取决于细胞核分割的质量。然而，细胞核尺寸变化、轮廓模糊、染色不均、细胞聚集及重叠等复杂问题带来了重大挑战。当前细胞核分割方法主要基于细胞核形态或轮廓。基于形态的方法泛化能力有限，难以有效预测不规则形状的细胞核；而基于轮廓的提取方法在准确分割重叠细胞核方面面临困难。为解决上述问题，我们提出了一种采用混合注意力残差U块的双分支网络用于细胞核实例分割。该网络同时预测目标信息与目标轮廓。此外，我们引入了一种后处理方法，结合目标信息与目标轮廓以区分重叠细胞核并生成实例分割图像。在网络中，我们提出了上下文融合块（CF-block），可有效提取并融合网络中的上下文信息。我们进行了大量定量评估以验证方法的性能。实验结果表明，在BNS、MoNuSeg、CoNSeg和CPM-17数据集上，所提方法相比现有最优方法表现出更优异的性能。