MBE-UNet: Multi-Branch Boundary Enhanced U-Net for Ultrasound Segmentation

Accurately capturing object areas in medical images is crucial for the clinical diagnosis and treatment of diseases. Due to the inherent low contrast and blurry edges in ultrasound images, most existing CNN-based methods often yield unsatisfactory segmentation results, making ultrasound image segmentation a challenging task. This paper introduces a novel multi-branch boundary enhanced network (MBE-UNet) for automatic ultrasound image segmentation. This method can accurately segment targets and delineate boundaries simultaneously using a multi-branch network. First, a global pyramid attention module (GPAM) is designed to capture multi-scale contextual information. Second, we embed a boundary cascade module (BCM) in the main branch to ensure the network focuses on edge information flow and generates relatively desirable boundaries. Finally, a boundary feature fusion module (BFM) is used to integrate boundary and region information, obtaining a boundary enhanced region map. The visual results and quantitative analysis demonstrate that the proposed MBE-UNet outperforms classical segmentation networks on three publicly available ultrasound datasets.