Integrating Multi-Scale Compression Attention with Edge Detection for Ultrasound Tumor Segmentation

Tumor segmentation is particularly important for ultrasound imaging-based diagnosis and therapy, such as breast cancer and gastrointestinal stromal tumor. However, the accurate ultrasound tumor segmentation remains challenging due to insufficient textures and edge features resulted from limited resolution, low signal-to-noise ratio (SNR), and artifacts in ultrasound images. To address the challenges, this paper proposes an novel tumor segmentation network that combines a multi-scale compression attention module (MCAM) and an edge detection module (EDM). MCAM fuses multi-scale features from a U-shaped backbone to capture global semantic features using a compression attention mechanism. EDM introduces multiple convolutional layers to extract the edge features of tumor. Furthermore, a semantic and texture fusion (STF) mechanism followed by an improved deep supervision is proposed to strengthen the network’s performance in resolving tumors. Experimental validations on a public dataset and a private dataset demonstrated the effectiveness of the proposed modules and the outperformance of our network over the current advanced segmentation networks in various metrics.