ICBNet: Iterative Context-Boundary Feedback Network for Polyp Segmentation

Accurate polyp segmentation from colonoscopy images, which is critical to automatic colorectal cancer diagnosis, attracts increasing attentions in recent years. Most existing deep learning-based methods adopt the one-stage processing pipeline, by usually fusing features from different levels or employing boundary-related attention. In this paper, we propose an novel Iterative Context-Boundary feedback Network, namely ICBNet, for robust and accurate polyp segmentation. By mimicking the "from-Preliminary-to-Refined" working paradigm of doctors, ICBNet adopts an iterative feedback learning strategy. Differently from other feedback methods which only use the prediction mask as a guide for foreground features, ICBNet refines encoder features with contextual and boundary-aware details from the preliminary segmentation and boundary predictions, and conducts such strategy in an iterative manner to achieve progressive improvement. Moreover, a dual-branch iterative feedback unit (IFU) is developed to enhance features under the guidance of segmentation and boundary predictions to enable the iterative learning. Extensive experiments on five widely-used polyp segmentation datasets demonstrate that the proposed ICBNet can utilize progressive refinement to effectively address the challenges of large appearance variations and obscure boundaries, and hence achieves more accurate and robust results against the state-of-the-arts methods.