APRNet: Alternative Prediction Refinement Network for Polyp Segmentation.

Colorectal cancer has become the second leading cause of cancer-related death, attracting considerable interest for automatic polyp segmentation in polyp screening system. Accurate segmentation of polyps from colonoscopy is a challenging task as the polyps diverse in color, size and texture while the boundary between polyp and background is sometimes ambiguous. We propose a novel alternative prediction refinement network (APRNet) to more accurately segment polyps. Based on the UNet architecture, our APRNet aims at exploiting all-level features by alternatively leveraging features from encoder and decoder branch. Specifically, a series of prediction residual refinement modules (PRR) learn the residual and progressively refine the segmentation at various resolution. The proposed APRNet is evaluated on two benchmark datasets and achieves new state-of-the-art performance with a dice of 91.33% and an accuracy of 97.31% on the Kvasir-SEG dataset, and a dice of 86.33% and an accuracy of 97.12% on the EndoScene dataset.Clinical relevance- This work proposes an automatic and accurate polyp segmentation algorithm that achieves new state- of-the-art performance, which can potentially act as an observer pointing out polyps in colonoscopy procedure.