Crowd-assisted polyp annotation of virtual colonoscopy videos

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 nonexpert 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 polypsper-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.

A Survey of Crowdsourcing in Medical Image Analysis

“…Classify (Albarqouni et al, 2016a), (Brady et al, 2014), (Brady et al, 2017), , (dos Reis et al, 2015), (Eickhoff, 2014), (Foncubierta Rodríguez and Müller, 2012), (Gur et al, 2017), (de Herrera et al, 2014), (Holst et al, 2015), (Huang and Hamarneh, 2017), (Keshavan et al, 2018), (Lawson et al, 2017), (Malpani et al, 2015), (Mavandadi et al, 2012, (Mitry et al, 2013), (Mitry et al, 2015), (Nguyen et al, 2012), (Park et al, 2016), (Park et al, 2017), (Smittenaar et al, 2018), (Sonabend et al, 2017), (Sullivan et al, 2018) Segment (Roethlingshoefer et al, 2017), (Boorboor et al, 2018), (Bruggemann et al, 2018), (Cabrera-Bean et al, 2017), (Chávez-Aragón et al, 2013), (Cheplygina et al, 2016), (Ganz et al, 2017), (Gurari et al, 2015b), (Heller et al, 2017), (Irshad et al, 2015), (Lee and Tufail, 2014), (Lee et al, 2016), (Lejeune et al, 2017), (Luengo-Oroz et al, 2012), (Maier-Hein et al, 2014a), (Maier-Hein et al, 2016), (O'Neil et al, 2017), (Park et al, 2018),…”

“…Abdomen (Roethlingshoefer et al, 2017), (Heim, 2018), (Heller et al, 2017), (Maier-Hein et al, 2014a), (Maier-Hein et al, 2014b), (Maier-Hein et al, 2015), (Maier-Hein et al, 2016), (McKenna et al, 2012), (Nguyen et al, 2012), (Park et al, 2016), (Park et al, 2017), (Park et al, 2018), (Rajchl et al, 2017) Brain (Ganz et al, 2017), (Keshavan et al, 2018), (Rajchl et al, 2016), (Sonabend et al, 2017), (Timmermans et al, 2016) Eye (Brady et al, 2014), (Brady et al, 2017), (Lee and Tufail, 2014), (Lee et al, 2016), (Leifman et al, 2015), (Mitry et al, 2013), (Mitry et al, 2015), (Mitry et al, 2016) Heart (Gur et al, 2017) Histo (Albarqouni et al, 2016a), (Albarqouni et al, 2016b), (Bruggemann et al, 2018), (Cabrera-Bean et al, 2017), (Della Mea et al, 2014), (dos Reis et al, 2015), (Eickhoff, 2014), (Irshad et al, 2015), (Irshad et al, 2017), (Lawson et al, 2017), (Luengo-Oroz et al, 2012), (Mavandadi et al, 2012), (Sameki et al, 2016), (Sharma et al, 2017), ...…”

A Survey of Crowdsourcing in Medical Image Analysis

“…The last type of data that is addressed is video, common for endoscopy and colonoscopy (both in the abdomen category). Several different approaches are used for presenting video data: 2D frames (Maier-Hein et al, 2014b, 2015Heim, 2018;Roethlingshoefer et al, 2017), 3D renderings (Nguyen et al, 2012;McKenna et al, 2012), short video clips (Park et al, 2017), or longer videos that can be paused and annotated (Park et al, 2018).…”

“…A refinement of this, used in 23% of surveyed papers, is to integrate separate test tasks in the tasks and exclude workers that fail the tests. Park et al (2018), for example, added a smiley face to colonoscopy videos to ensure attention.…”

C2A: Crowd consensus analytics for virtual colonoscopy

“…We recently conducted a preliminary study [27] to leverage the (a) (b) Figure 3: The (a) supine and (b) prone colons, and the corresponding computed centerlines (shown in pink) for creating the virtual flythroughs in VC, from rectum to cecum (antegrade direction) and cecum to rectum (retrograde direction).…”