Crowd-Assisted Polyp Annotation of Virtual Colonoscopy Videos

TL;DR

This paper explores using crowdsourcing with non-experts to annotate polyps in virtual colonoscopy videos, aiming to assist radiologists and reduce diagnosis time.

Contribution

It introduces a crowdsourcing workflow for polyp annotation in VC videos and demonstrates its high sensitivity, especially for larger polyps.

Findings

Achieved 87.88% sensitivity for polyps per patient.

Non-experts effectively detect and annotate polyps.

Potential to aid radiologists in VC examinations.

Abstract

Virtual colonoscopy (VC) allows a radiologist to navigate through a 3D colon model reconstructed from a computed tomography scan of the abdomen, looking for polyps, the precursors of colon cancer. Polyps are seen as protrusions on the colon wall and haustral folds, visible in the VC fly-through videos. A complete review of the colon surface requires full navigation from the rectum to the cecum in antegrade and retrograde directions, which is a tedious task that takes an average of 30 minutes. Crowdsourcing is a technique for non-expert users to perform certain tasks, such as image or video annotation. In this work, we use crowdsourcing for the examination of complete VC fly-through videos for polyp annotation by non-experts. The motivation for this is to potentially help the radiologist reach a diagnosis in a shorter period of time, and provide a stronger confirmation of the eventual diagnosis. The crowdsourcing interface includes an interactive tool for the crowd to annotate suspected polyps in the video with an enclosing box. Using our workflow, we achieve an overall polyps-per-patient sensitivity of 87.88% (95.65% for polyps $\geq$5mm and 70% for polyps $<$5mm). We also demonstrate the efficacy and effectiveness of a non-expert user in detecting and annotating polyps and discuss their possibility in aiding radiologists in VC examinations.