Learnable Oriented-Derivative Network for Polyp Segmentation

Gastrointestinal polyps are the main cause of colorectal cancer. Given the polyp variations in terms of size, color, texture and poor optical conditions brought by endoscopy, polyp segmentation is still a challenging problem. In this paper, we propose a Learnable Oriented-Derivative Network (LOD-Net) to refine the accuracy of boundary predictions for polyp segmentation. Specifically, it firstly calculates eight oriented derivatives at each pixel for a polyp. It then selects those pixels with large oriented-derivative values to constitute a candidate border region of a polyp. It finally refines boundary prediction by fusing border region features and also those high-level semantic features calculated by a backbone network. Extensive experiments and ablation studies show that the proposed LOD-Net achieves superior performance compared to the state-of-the-art methods by a significant margin on publicly available datasets, including CVC-ClinicDB, CVC-ColonDB, Kvasir, ETIS, and EndoScene. For examples, for the dataset Kvasir, we achieve an mIoU of 88.5% vs. 82.9% by PraNet; for the dataset ETIS, we achieve an niloU of 88.4% vs. 72.7% by PraNet. The code is available at https://github.com/midsdsy/LOD-Net.