Polyp Segmentation of Colonoscopy Images by Exploring the Uncertain Areas

Abstract

Colorectal cancer is one of the leading causes of death worldwide. Polyps are early symptoms of colorectal cancer and prone to malignant transformation. Polyp segmentation of colonoscopy images can help diagnosis. However, existing studies on polyp segmentation of colonoscopy images face two main difficulties: blurry polyp boundaries, close resemblances between polyps and surrounding tissues. The former may lead to partial segmentations, while the latter can result in false positive segmentations. This paper proposes a new polyp segmentation framework to tackle the two challenges. In this method, an uncertainty region based module called Uncertainty eXploration (UnX) is introduced to get the complete polyp region while eliminating the interferences from the backgrounds. Specifically, it refines the feature maps with ternary guidance masks by dividing the initial guidance maps into three types: foreground, background and uncertain region, so that the uncertain areas are highlighted for more foreground objects while the backgrounds are forcefully suppressed to avoid interferences of tissues in background. Taking UnX as side supervision to the transformer encoder based backbone stages, the proposed method can mine the boundary areas from the uncertainty regions gradually and obtain robust polyp segmentation finally. Moreover, a new module called Feature Enhancement (FeE) is also incorporated in the framework to enhance the discrimination for images with significant variation of sizes and shapes of polyps. FeE can supply multi-scale features to the global oriented transformer features. Experiments on five polyp segmentation benchmark datasets of colonoscopy images, Kvasir, CVC-ClinicDB, ETIS, CVC-ColonDB and CVC-300, show the superior performances of our proposed method. Especially, for ETIS, the most challenging among the five datasets, our method achieves 7.7% and 5.6% improvements in mDSC (mean Dice Similarity Coefficient) and mIoU (mean Intersection over Union) respectively in comparison with the state-of-the-arts methods.

Keywords

• Polyp segmentation
• deep learning
• colonoscopy image
• colorectal cancer