Purpose/Significance The traditional methods for segmenting pleural effusion heavily rely on prior knowledge,are cum-bersome in process,time-consuming,and often exhibit poor performance.There is a need to enhance efficiency and accuracy in ad-dressing 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 co-efficient on the test set reaches 88.76%for ARU-Net,and shows significant advantages in segmentation integrity and accuracy com-pared to U-Net and ResU-Net,which can meet clinical requirements.
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