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Although deep learning plays an important role in cell nucleus segmentation, it still faces problems such as difficulty in extracting subtle features and blurring of nucleus edges in pathological diagnosis. Aiming at the above problems, a nuclear segmentation network combined with attention mechanism is proposed. The network uses UNet network as the basic structure and the depth separable residual (DSRC) module as the feature encoding to avoid losing the boundary information of the cell nucleus. The feature decoding uses the coordinate attention (CA) to enhance the long-range distance in the feature space and highlights the key information of the nuclear position. Finally, the semantics information fusion (SIF) module integrates the feature of deep and shallow layers to improve the segmentation effect. The experiments were performed on the 2018 data science bowl (DSB2018) dataset and the triple negative breast cancer (TNBC) dataset. For the two datasets, the accuracy of the proposed method was 92.01% and 89.80%, the sensitivity was 90.09% and 91.10%, and the mean intersection over union was 89.01% and 89.12%, respectively. The experimental results show that the proposed method can effectively segment the subtle regions of the nucleus, improve the segmentation accuracy, and provide a reliable basis for clinical diagnosis.深度学习在细胞核分割中具有重要作用，但在病理诊断中仍面临着细胞核图像的细微特征难以提取、核边缘模糊等问题。针对上述问题，本文提出了一种结合注意力机制的细胞核分割网络。该网络使用U型网络（UNet）作为基本结构，以深度可分离残差卷积（DSRC）模块作为特征编码，避免丢失细胞核边界信息；特征解码引入坐标注意力（CA）加强特征空间上远程距离，突出细胞核位置的关键信息；最后，设计语义信息融合（SIF）模块整合深浅层特征，改善分割效果。在2018数据科学碗（DSB2018）和三阴乳腺癌（TNBC）数据集上分别进行实验，所提方法的精确率在两个数据集上分别为92.01%、89.21%，灵敏度为90.09%、91.10%，平均交并比为89.01%、89.12%。实验结果表明，本文所提方法能有效分割细胞核细微区域，提升分割准确度，为临床诊断提供可靠依据。. |
