Estimating the coverage in 3d reconstructions of the colon from colonoscopy videos

TL;DR

This paper introduces a new method to estimate the coverage of reconstructed colon surfaces from colonoscopy videos, aiming to identify missed regions and improve detection of polyps during procedures.

Contribution

The work presents a novel segmentation-based approach to quantify local and global coverage in 3D colon reconstructions, with demonstrated accuracy on synthetic and real data.

Findings

Mean absolute coverage error of 3-6% on synthetic and CT data

Qualitative success on real colonoscopy videos

Effective segmentation of colon into regions for coverage estimation

Abstract

Colonoscopy is the most common procedure for early detection and removal of polyps, a critical component of colorectal cancer prevention. Insufficient visual coverage of the colon surface during the procedure often results in missed polyps. To mitigate this issue, reconstructing the 3D surfaces of the colon in order to visualize the missing regions has been proposed. However, robustly estimating the local and global coverage from such a reconstruction has not been thoroughly investigated until now. In this work, we present a new method to estimate the coverage from a reconstructed colon pointcloud. Our method splits a reconstructed colon into segments and estimates the coverage of each segment by estimating the area of the missing surfaces. We achieve a mean absolute coverage error of 3-6\% on colon segments generated from synthetic colonoscopy data and real colonography CT scans. In addition, we show good qualitative results on colon segments reconstructed from real colonoscopy videos.