Triple branch deep network for polyp image segmentation

Medical image segmentation is essential for accurately extracting tissue structures or pathological regions from medical images. However, medical image segmentation methods are often influenced by factors such as image noise and irregular shapes, making precise segmentation challenging. To tackle these challenges, this paper proposes a triple-branch medical image segmentation network (TBIB-Net) that incorporates implicit boundary priors. The boundary map, acquired by a boundary detection algorithm, is used to restrict the results of the boundary branch. Extensive experiments indicate that TBIB-Net achieves state-of-the-art performance on publicly available polyp datasets.