A Multi-Task Segmentation and Classification Network Based on Ultrasound Images for Predicting the Grading of Ascites in the Abdominal Cavity

Abdominal trauma with bleeding is a leading cause of post-traumatic death, and detecting free fluid in the abdomen or hemoperitoneum can provide critical guidance for clinical management. Rapid and accurate diagnosis of abdominal bleeding using ultrasound is significant for making decisions regarding the need for surgical intervention. This study introduces a multi-task network for the segmentation and classification of ascites in ultrasound images. The network utilizes a U-Net backbone with a ResNext encoder as the basic architecture for the segmentation and classification models. The segmentation network includes a Frequency Channel Attention (FCA) attention module, which effectively broadens the range of captured information and enhances the robustness of channel representation. Furthermore, an Enhanced Channel Attention Multi Feature Fusion (EMFF) was used to extract the interdependencies between feature channels by combining high-order and low-order feature mappings, thereby improving segmentation accuracy. Lastly, a classification branch was created to classify ascites by sharing encoder features. Experiments on the collected ascites ultrasound dataset demonstrated that the proposed method achieved a segmentation Dice of 85.28% and a classification accuracy of 86.18%. It outperformed the leading multi-task SOTA method by 0.7% in Dice and 2.03% in accuracy, establishing a new benchmark for simultaneous ascites assessment. This study showed that the proposed network is valuable for the preliminary diagnosis of ascites in ultrasound and can serve as a potential auxiliary tool for clinical ascites examination in emergency situations.