Automatic polyp detection in colonoscopy videos

Zijie Yuan, Mohammadhassan Izadyyazdanabadi, Divya Mokkapati, Rujuta Panvalkar, Jae Y. Shin, Nima Tajbakhsh, Suryakanth Gurudu, Jianming Liang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Colon cancer is the second cancer killer in the US [1]. Colonoscopy is the primary method for screening and prevention of colon cancer, but during colonoscopy, a significant number (25% [2]) of polyps (precancerous abnormal growths inside of the colon) are missed; therefore, the goal of our research is to reduce the polyp miss-rate of colonoscopy. This paper presents a method to detect polyp automatically in a colonoscopy video. Our system has two stages: Candidate generation and candidate classification. In candidate generation (stage 1), we chose 3,463 frames (including 1,718 with-polyp frames) from real-time colonoscopy video database. We first applied processing procedures, namely intensity adjustment, edge detection and morphology operations, as pre-preparation. We extracted each connected component (edge contour) as one candidate patch from the pre-processed image. With the help of ground truth (GT) images, 2 constraints were implemented on each candidate patch, dividing and saving them into polyp group and non-polyp group. In candidate classification (stage 2), we trained and tested convolutional neural networks (CNNs) with AlexNet architecture [3] to classify each candidate into with-polyp or non-polyp class. Each with-polyp patch was processed by rotation, translation and scaling for invariant to get a much robust CNNs system. We applied leave-2-patients-out cross-validation on this model (4 of 6 cases were chosen as training set and the rest 2 were as testing set). The system accuracy and sensitivity are 91.47% and 91.76%, respectively.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationImage Processing
PublisherSPIE
Volume10133
ISBN (Electronic)9781510607118
DOIs
StatePublished - 2017
EventMedical Imaging 2017: Image Processing - Orlando, United States
Duration: Feb 12 2017Feb 14 2017

Other

OtherMedical Imaging 2017: Image Processing
CountryUnited States
CityOrlando
Period2/12/172/14/17

Keywords

  • Colonoscopy
  • Computer-aided detection
  • Convolutional neural networks (CNNs)
  • Deep learning
  • Polyp

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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