SATU-net: a shadow adaptive tracing U-net for gastric cavity segmentation based on the principle of ultrasound imaging.

Accurate segmentation of gastric cavities from ultrasound images remains a challenging task due to the presence of ultrasound shadow and varying anatomical structures. To address these challenges, we collected a Gastric Ultrasound Image (GUSI) dataset using transabdominal techniques, after administering an echoic cellulose-based gastric ultrasound contrast agent (TUS-OCCA), and annotated the gastric cavity regions. We propose a model called Shadow Adaptive Tracing U-net (SATU-net) for gastric cavity segmentation on the GUSI dataset. SATU-net is specifically designed for gastric cavity segmentation in ultrasound images. The method introduces an Adaptive Shadow Tracing Module (ASTM), Shadow Separation Module (SSM), and an affine transformation mechanism to mitigate the impact of ultrasound shadow. The affine transformation aligns ultrasound image regions to reduce geometric distortion, while the ASTM dynamically tracks and compensates for ultrasound shadow, and the SSM extracts the shadow separation image. Extensive experiments on the gastric ultrasound dataset demonstrate that SATU-net achieves superior segmentation performance compared to several state-of-the-art deep learning methods, with an IoU improvement of 2.26% over the second-best competitor. Further robustness analysis and limited external validation provide preliminary evidence that SATU-net generalizes across diverse clinical scenarios. Our method provides a robust solution for ultrasound image segmentation and can be extended to other medical imaging tasks. Additionally, the ASTM module can be flexibly applied to existing network frameworks.