FET-UNet: Merging CNN and transformer architectures for superior breast ultrasound image segmentation

Purpose

Breast cancer remains a significant cause of mortality among women globally, highlighting the critical need for accurate diagnosis. Although Convolutional Neural Networks (CNNs) have shown effectiveness in segmenting breast ultrasound images, they often face challenges in capturing long-range dependencies, particularly for lesions with similar intensity distributions, irregular shapes, and blurred boundaries. To overcome these limitations, we introduce FET-UNet, a novel hybrid framework that integrates CNNs and Swin Transformers within a UNet-like architecture.

Methods

FET-UNet features parallel branches for feature extraction: one utilizes ResNet34 blocks, and the other employs Swin Transformer blocks. These branches are fused using an advanced feature aggregation module (AFAM), enabling the network to effectively combine local details and global context. Additionally, we include a multi-scale upsampling mechanism in the decoder to ensure precise segmentation outputs. This design enhances the capture of both local details and long-range dependencies.

Results

Extensive evaluations on the BUSI, UDIAT, and BLUI datasets demonstrate the superior performance of FET-UNet compared to state-of-the-art methods. The model achieves Dice coefficients of 82.9% on BUSI, 88.9% on UDIAT, and 90.1% on BLUI.

Conclusion

FET-UNet shows great potential to advance breast ultrasound image segmentation and support more precise clinical diagnoses. Further research could explore the application of this framework to other medical imaging modalities and its integration into clinical workflows.