DBF-Net: A Dual-Branch Network with Feature Fusion for Ultrasound Image Segmentation

The inherent ambiguity in distinguishing boundaries between lesions and adjacent tissues makes accurate segmentation in ultrasound images challenging. Although deep learning has improved segmentation accuracy, boundary segmentation quality and its relationship with anatomical structures remain underexplored. To address this, we propose DBF-Net, a dual-branch deep neural network that captures supervised relationships between anatomical structures and boundaries. Additionally, we introduce a feature fusion module to enhance the integration of body and boundary information. We evaluate DBF-Net on three public ultrasound image datasets and demonstrate its superiority over existing methods, achieving Dice Similarity Coefficients of 81.05±10.44% for breast cancer, 76.41±5.52% for brachial plexus nerves, and 87.75±4.18% for infantile hemangiomas on the BUSI, UNS, and UHES datasets, respectively. Our approach outperforms current methods, showcasing its effectiveness in ultrasound image segmentation. Code available at: https://github.com/apple1986/DBF-Net.