A dual-branch and dual attention transformer and CNN hybrid network for ultrasound image segmentation

Ultrasound imaging has become a crucial tool in medical diagnostics, offering real-time visualization of internal organs and tissues. However, challenges such as low contrast, high noise levels, and variability in image quality hinder accurate interpretation. To enhance the diagnostic accuracy and support treatment decisions, precise segmentation of organs and lesions in ultrasound image is essential. Recently, several deep learning methods, including convolutional neural networks (CNNs) and Transformers, have reached significant milestones in medical image segmentation. Nonetheless, there remains a pressing need for methods capable of seamlessly integrating global context with local fine-grained information, particularly in addressing the unique challenges posed by ultrasound images.

In this paper, to address these issues, we propose DDTransUNet, a hybrid network combining Transformer and CNN, with a dual-branch encoder and dual attention mechanism for ultrasound image segmentation. DDTransUNet adopts a Swin Transformer branch and a CNN branch to extract global context and local fine-grained information. The dual attention comprising Global Spatial Attention (GSA) and Global Channel Attention (GCA) modules to capture long-range visual dependencies. A novel Cross Attention Fusion (CAF) module effectively fuses feature maps from both branches using cross-attention.

Experiments on three ultrasound image datasets demonstrate that DDTransUNet outperforms previous methods. In the TN3K dataset, DDTransUNet achieves IoU, Dice, HD95 and ACC metrics of 73.82%, 82.31%, 16.98 mm, and 96.94%, respectively. In the BUS-BRA dataset, DDTransUNet achieves 80.75%, 88.23%, 8.12 mm, and 98.00%. In the CAMUS dataset, DDTransUNet achieves 82.51%, 90.33%, 2.82 mm, and 96.87%.

These results indicate that our method can provide valuable diagnostic assistance to clinical practitioners.