【摘要】 目的/意义解决传统胸腔积液分割方法严重依赖先验知识、流程烦琐、耗时费力、性能不佳等问题，提高效率和准确率。方法/过程根据胸部CT图像的积液特征，提出一种基于残差注意力机制的胸腔积液分割模型ARU-Net。以U-Net模型为主干网络，在编码和解码阶段引入残差注意力单元，有效获取图像上下文信息，提高对特征的利用率。结果/结论在测试集上的DICE相似系数达到88.76%,与U-Net和ResU-Net相比在分割完整性和准确性方面具有显著优势，能够满足临床需求。更多还原

【Abstract】 Purpose/Significance The traditional methods for segmenting pleural effusion heavily rely on prior knowledge, are cumbersome in process, time-consuming, and often exhibit poor performance. There is a need to enhance efficiency and accuracy in addressing these issues. Method/Process Based on the characteristics of pleural effusion in chest CT images, the paper proposes a pleural effusion segmentation model called ARU-Net, which is based on the residual attention mechanism. The ARU-Net model utilizes the U-Net architecture as its backbone network. It introduces residual attention units in both the encoding and decoding stages to effectively capture contextual information from the images, thereby improving the utilization of features. Result/Conclusion The DICE similarity coefficient on the test set reaches 88.76% for ARU-Net, and shows significant advantages in segmentation integrity and accuracy compared to U-Net and ResU-Net, which can meet clinical requirements.更多还原