Colorectal Polyp Segmentation by U-Net with Dilation Convolution

TL;DR

This paper introduces a novel deep learning framework with dilated convolution for accurate colorectal polyp segmentation during colonoscopy, significantly improving early detection of colorectal cancer.

Contribution

It proposes a new end-to-end U-Net based model with dilated convolution and a simplified decoder for enhanced polyp segmentation accuracy.

Findings

Achieves state-of-the-art results on CVC-ClinicDB

Improves detection performance with post-processing techniques

Utilizes dilated convolution for high-level semantic feature learning

Abstract

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and a leading cause of cancer deaths in the United States. Colorectal polyps that grow on the intima of the colon or rectum is an important precursor for CRC. Currently, the most common way for colorectal polyp detection and precancerous pathology is the colonoscopy. Therefore, accurate colorectal polyp segmentation during the colonoscopy procedure has great clinical significance in CRC early detection and prevention. In this paper, we propose a novel end-to-end deep learning framework for the colorectal polyp segmentation. The model we design consists of an encoder to extract multi-scale semantic features and a decoder to expand the feature maps to a polyp segmentation map. We improve the feature representation ability of the encoder by introducing the dilated convolution to learn high-level semantic features without resolution reduction. We further design a simplified decoder which combines multi-scale semantic features with fewer parameters than the traditional architecture. Furthermore, we apply three post processing techniques on the output segmentation map to improve colorectal polyp detection performance. Our method achieves state-of-the-art results on CVC-ClinicDB and ETIS-Larib Polyp DB.