Automated polyp detection in colonoscopy videos using shape and context information

Nima Tajbakhsh, Suryakanth R. Gurudu, Jianming Liang

    Research output: Contribution to journalArticlepeer-review

    190 Scopus citations

    Abstract

    This paper presents the culmination of our research in designing a system for computer-aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps. Specifically, given a colonoscopy image, we first obtain a crude edge map. Second, we remove non-polyp edges from the edge map using our unique feature extraction and edge classification scheme. Third, we localize polyp candidates with probabilistic confidence scores in the refined edge maps using our novel voting scheme. The suggested CAD system has been tested using two public polyp databases, CVC-ColonDB, containing 300 colonoscopy images with a total of 300 polyp instances from 15 unique polyps, and ASU-Mayo database, which is our collection of colonoscopy videos containing 19,400 frames and a total of 5,200 polyp instances from 10 unique polyps. We have evaluated our system using free-response receiver operating characteristic (FROC) analysis. At 0.1 false positives per frame, our system achieves a sensitivity of 88.0% for CVC-ColonDB and a sensitivity of 48% for the ASU-Mayo database. In addition, we have evaluated our system using a new detection latency analysis where latency is defined as the time from the first appearance of a polyp in the colonoscopy video to the time of its first detection by our system. At 0.05 false positives per frame, our system yields a polyp detection latency of 0.3 seconds.

    Original languageEnglish (US)
    Article number2487997
    Pages (from-to)630-644
    Number of pages15
    JournalIEEE Transactions on Medical Imaging
    Volume35
    Issue number2
    DOIs
    StatePublished - Feb 1 2016

    Keywords

    • Boundary classification
    • Detection latency
    • Edge voting
    • Optical colonoscopy
    • Polyp detection

    ASJC Scopus subject areas

    • Software
    • Radiological and Ultrasound Technology
    • Computer Science Applications
    • Electrical and Electronic Engineering

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