Detecting blindspots in colonoscopy by modelling curvature

Abstract

Optical colonoscopy is the gold standard for colorectal cancer screening, however even under optimal conditions only 81% of the internal tissue is inspected, in part causing up to 22% of early adenomas to be missed. Blindspots commonly occur at acute bends where the camera’s view is blocked. 3D reconstruction alone is insufficient to assess screening completeness as predictions of both the seen and unseen mucosa are required. Existing works in blindspot detection nevertheless use a highly detailed 3D reconstruction as the first step, the complexity of which degrades processing speed and reliability. We demonstrate that this complexity is not needed to predict whether acute bends have been adequately inspected. We propose a parametric model of the colon with only 2 variables: radius and curvature. By incorporating curvature, our method can predict the occlusion which acute bends cause. We use CT scans from 12 patients which on average contain 20 bends. We use a custom colonoscopy simulator and, assuming known geometry, show that a curved model reliably predicts these blindspots while a non-curved model always misses them. From our frame-by-frame predictions we build a panoramic map of the tissue inspected over the procedure. We show that this correctly identifies all 10 blindspots during 3 minutes of colonoscopy. We envisage that by alerting clinicians to mucosa which they have missed in real time, they can revisit these areas, improving the detection of polyps and cancers.