US-Net: U-shaped network with Convolutional Attention Mechanism for ultrasound medical images

Abstract

Ultrasound imaging, characterized by low contrast, high noise, and interference from surrounding tissues, poses significant challenges in lesion segmentation. To tackle these issues, we introduce an enhanced U-shaped network that incorporates several novel features for precise, automated segmentation. Firstly, our model utilizes a convolution-based self-attention mechanism to establish long-range dependencies in feature maps, crucial for small dataset applications, accompanied by a soft thresholding method for noise reduction. Secondly, we employ multi-sized convolutional kernels to enrich feature processing, coupled with curvature calculations to accentuate edge details via a soft-attention approach. Thirdly, an advanced skip connection strategy is implemented in the UNet architecture, integrating information entropy to assess and utilize texture-rich channels, thereby improving semantic detail in the encoder before merging with decoder outputs. We validated our approach using a newly curated dataset, VPUSI (Vascular Plaques Ultrasound Images), alongside the established datasets, BUSI, TN3K and DDTI. Comparative experiments on these datasets show that our model outperforms existing state-of-the-art techniques in segmentation accuracy.