Comparative performance of 3D machine-learning and deep-learning models in the detection of small polyps in dual-energy CT colonography

Janne J. Näppi; Tomoki Uemura; Se Hyung Kim; Hyoungseop Kim; Hiroyuki Yoshida

doi:10.1117/12.2549793

16 March 2020 Comparative performance of 3D machine-learning and deep-learning models in the detection of small polyps in dual-energy CT colonography

Janne J. Näppi, Tomoki Uemura, Se Hyung Kim, Hyoungseop Kim, Hiroyuki Yoshida

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113143C (2020) https://doi.org/10.1117/12.2549793
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Colorectal cancer is the second leading cause of cancer deaths worldwide. Computed tomographic colonography (CTC) can detect large colorectal polyps and cancers at a high sensitivity, whereas it can miss some of the smaller but still clinically significant 6 – 9 mm polyps. Dual-energy CTC (DE-CTC) can be used to provide more detailed information about scanned materials than does conventional single-energy CTC. We compared the classification performance of a 3D convolutional neural network (DenseNet) with those of four traditional 3D machine-learning models (AdaBoost, support vector machine, random forest, Bayesian neural network) and their cascade and ensemble classifier variants in the detection of small polyps in DE-CTC. Twenty patients with colonoscopy-confirmed polyps were examined by DE-CTC with a reduced one-day bowel preparation. The traditional machine-learning models were designed to identify polyps based on native radiomic dual-energy features of the DE-CTC image volumes. The performance of the machine-learning models was evaluated by use of the leave-one-patient-out method. The DenseNet was trained with a large independent external dataset of single-energy CTC cases and tested on blended image volumes of the DE-CTC cases. Although the DenseNet yielded the highest detection accuracy for typical polyps, AdaBoost and its cascade classifier variant yielded the highest overall polyp detection performance.

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Janne J. Näppi, Tomoki Uemura, Se Hyung Kim, Hyoungseop Kim, and Hiroyuki Yoshida "Comparative performance of 3D machine-learning and deep-learning models in the detection of small polyps in dual-energy CT colonography", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143C (16 March 2020); https://doi.org/10.1117/12.2549793

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