SDC-Net: Semi-supervised breast ultrasound lesion segmentation via semantic decoupling

Semi-supervised breast ultrasound (BUS) lesion boundary segmentation is a promising technique to enhance model generalization, with the potential to address the challenges of high annotation costs and data scarcity in medical imaging. However, existing semi-supervised strategies face significant challenges in this domain due to the low contrast and difficulty in differentiating between breast ultrasound lesion areas and the background. To address this, we propose a novel semi-supervised strategy for breast ultrasound lesion boundary segmentation. Unlike traditional approaches that rely on visual feature understanding, we redefine the semi-supervised segmentation problem as semantic decoupling between foreground and background in lesion images. Based on this insight, we proposed a text-guided semi-supervised segmentation framework for breast ultrasound lesions. It first learns disentangled representations through contrastive learning between text and image features of foreground and background, then enhances the semantic understanding of the image encoder through supervised learning with partially labeled data. Subsequently, the pretrained encoder-decoder is guided by weak prompts on unlabeled data to generate robust pseudo-labels, progressively achieving semantic decoupling of foreground and background for lesion segmentation. We validated the effectiveness of this method on three publicly available breast ultrasound datasets, achieving consistently superior segmentation performance compared with existing semi-supervised approaches. The code will be released here.