Dual-branch body and boundary supervision network for ultrasound image segmentation

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A sharp prediction for ultrasound image segmentation is critical to assist doctors in the accurate diagnosis and treatment. Although the existing methods could achieve impressive performance on biomedical image segmentation, they still have difficulty in segmenting pixels near the boundaries of lesions faithfully. To this end, we propose a body and boundary supervision network aiming to improve ultrasound image segmentation performance, especially on the boundary of the segmented objects. In contrast to existing approaches that take lesions as a whole, a novel dual-branch supervision block was developed to explore explicit modeling of the body and boundary of the object at feature level. Furthermore, we presented an adaptive feature fusion strategy to combine feature maps from dual-branch to get more representative features for final segmentation. Experimental results on three public ultrasound datasets including BUSI, HC18 and Hemangioma demonstrate that the proposed network can achieve leading segmentation performance for breast cancer, fetal head circumference and hemangioma on ultrasound images compared with the existing state-of-the-art models.