Double U-Net: semi-supervised ultrasound image segmentation combining CNN and transformer's U-shaped network

Ultrasound image segmentation remains challenging due to blurred boundaries and morphological heterogeneity, while existing deep learning methods heavily rely on costly expert annotations. To address these issues, this study proposes a semi-supervised learning algorithm called Double U-Net (W-Net), built on consistency regularization and a cross-teaching framework. Specifically, we introduce a Deeper Dual-output Fusion U-Net (DDFU-Net) designed to tackle ultrasound-specific challenges. This architecture enhances multi-scale feature extraction by improving the backbone network, integrating a dual-output refinement (DOR) module and incorporating a spatial feature calibration (SFC) module to optimize multi-scale feature fusion. Furthermore, the proposed network combines DDFU-Net with a lightweight Transformer, enabling CNNs and Transformers to complement each other in local and global feature extraction. Through mutual end-to-end supervision, the method effectively leverages unlabeled data. Our method achieves competitive performance: (1) Compared to other semi-supervised methods, it outperforms the second-best by 7.96% (BUSI, 20% labels) and 17.52% (10% labels), with 5.47% (GCUI, 20%) and 6.08% (GCUI, 10%) improvements; and (2) compared to fully supervised U-Net, it elevates Dice by 6.09%/3.86% (BUSI) and 3.89%/4.42% (GCUI) under 10%/20% labels condition, proving the ability to effectively leverage unlabeled data, extracting rich feature information to enhance model interpretability of complex medical images, particularly in low-data scenarios.