Crowd-assisted polyp annotation of virtual colonoscopy videos

Ji Hwan Park; Saad Nadeem; Joseph Marino; Kevin Baker; Matthew Barish; Arie Kaufman

doi:10.1117/12.2292899

6 March 2018 Crowd-assisted polyp annotation of virtual colonoscopy videos

Ji Hwan Park, Saad Nadeem, Joseph Marino, Kevin Baker, Matthew Barish, Arie Kaufman

Proceedings Volume 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications; 105790M (2018) https://doi.org/10.1117/12.2292899
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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 y-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 y-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 work flow, we achieve an overall polyps-per-patient sensitivity of 87.88% (95.65% for polyps ≥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.

Conference Presentation

Citation Download Citation

Ji Hwan Park, Saad Nadeem, Joseph Marino, Kevin Baker, Matthew Barish, and Arie Kaufman "Crowd-assisted polyp annotation of virtual colonoscopy videos", Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105790M (6 March 2018); https://doi.org/10.1117/12.2292899

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