Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks

TL;DR

This paper introduces a deep learning framework using convolutional neural networks and shape-from-shading to improve automatic polyp detection and segmentation in colonoscopy images, achieving state-of-the-art results.

Contribution

It is the first to combine fully convolutional networks with shape-from-shading and RGB data for enhanced polyp segmentation and detection in colonoscopy images.

Findings

Achieved mean segmentation IU of 47.78% and 56.95% on two datasets.

Surpassed state-of-the-art detection recall of over 90%.

Demonstrated improved performance with depth information incorporated.

Abstract

Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), fine-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the first work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance