SwinT-Unet: Ultrasound Image Segmentation Based on Two-Channel Self-Attention Mechanism

Ultrasound image segmentation plays a key role in disease diagnosis and treatment, but accurately seg⁃ menting the regions of interest is still a challenging task due to the low contrast, noise interference, and variability in shape, size, and location of the lesions in ultrasound images. To address this problem, we propose a dual-channel self-attention mechanism U-shaped network (SwinT-Unet), which utilizes Swin-Transformer and Unet encoder to simultaneously extract features. To effectively fuse the different-level features extracted by Swin-Transformer and Unet encoder, we also propose a gated dual-layer feature fusion module (GDFF), which achieves the effective fusion of global and local features through the gating mechanism, thereby improving the accuracy and robustness of the segmentation results. We conduct experiments on two different ultrasound image datasets, and the results show that our proposed model outperforms the existing convolution⁃ al neural network and Transformer-based models in terms of segmentation accuracy and robustness. Our paper provides a new method for ultrasound image segmentation, and offers more accurate and reliable support for clinical medical diagnosis and treatment. © 2024 Chinese Institute of Electronics. All rights reserved.