Computer-aided classification of colorectal polyps using blue-light and linked-color imaging

Thom Scheeve; Ramon-Michel Schreuder; Fons van der Sommen; Joep E. G. IJspeert; Evelien Dekker; Erik J. Schoon; Peter H. N. De With

doi:10.1117/12.2508223

13 March 2019 Computer-aided classification of colorectal polyps using blue-light and linked-color imaging

Thom Scheeve, Ramon-Michel Schreuder, Fons van der Sommen, Joep E. G. IJspeert, Evelien Dekker, Erik J. Schoon, Peter H. N. De With

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Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 1095012 (2019) https://doi.org/10.1117/12.2508223
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths. Since most CRCs develop from colorectal polyps (CRPs), accurate endoscopic differentiation facilitates decision making on resection of CRPs, thereby increasing cost-efficiency and reducing patient risk. Current classification systems based on whitelight imaging (WLI) or narrow-band imaging (NBI) have limited predictive power, or they do not consider sessile serrated adenomas/polyps (SSA/Ps), although these cause up to 30% of all CRCs. To better differentiate adenomas, hyperplastic polyps, and SSA/Ps, this paper explores the feasibility of two approaches: (1) an accurate computer-aided diagnosis (CADx) system for automated diagnosis of CRPs, and (2) novel endoscopic imaging techniques like blue-light imaging (BLI) and linked-color imaging (LCI). Two methods are explored to predict histology: (1) direct classification using a support vector machine (SVM) classifier, and (2) classification via a clinical classification model (WASP classification) combined with an SVM. The use of probabilistic features of SVM facilitates objective quantification of the detailed classification process. Automated differentiation of colonic polyp subtypes reaches accuracies of 78−96%, thereby improving medical expert results by 4−20%. Diagnostic accuracy for directly predicting adenomatous from hyperplastic histology reaches 93% and 87−90% using NBI and the novel BLI and LCI techniques, respectively, thus improving medical expert results by 26% and 20−23%, respectively. Predicting adenomatous histology in diminutive polyps with high confidence yields NPVs of 100%, clearly satisfying the PIVI guideline recommendation on endoscopic innovations (≥90% NPV). Our CADx system outperforms clinicians, while the novel BLI technique adds performance value.

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Thom Scheeve, Ramon-Michel Schreuder, Fons van der Sommen, Joep E. G. IJspeert, Evelien Dekker, Erik J. Schoon, and Peter H. N. De With "Computer-aided classification of colorectal polyps using blue-light and linked-color imaging", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095012 (13 March 2019); https://doi.org/10.1117/12.2508223

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KEYWORDS

Endoscopy

Imaging systems

Diagnostics

Computer aided diagnosis and therapy

Image classification

Machine learning

Colorectal cancer

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