Ms. Zijie Yuan Profile

Zijie Yuan

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Proceedings Article | 24 February 2017 Paper

Automatic polyp detection in colonoscopy videos

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

Proceedings Volume 10133, 101332K (2017) https://doi.org/10.1117/12.2254671

KEYWORDS: Convolutional neural networks, Linear filtering, Video, Colorectal cancer, Neural networks, Computer-aided diagnosis, Cancer, Colon, Classification systems, Databases, Edge detection, Machine learning, Video acceleration, Systems engineering

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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.

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