A novel clinical gland feature for detection of early Barrett’s neoplasia using volumetric laser endomicroscopy

Thom Scheeve; Maarten R. Struyvenberg; Wouter L. Curvers; Albert J. de Groof; Erik J. Schoon; Jacques J. G. H. M. Bergman; Fons van der Sommen; Peter H. N. de With

doi:10.1117/12.2508244

13 March 2019 A novel clinical gland feature for detection of early Barrett’s neoplasia using volumetric laser endomicroscopy

Thom Scheeve, Maarten R. Struyvenberg, Wouter L. Curvers, Albert J. de Groof, Erik J. Schoon, Jacques J. G. H. M. Bergman, Fons van der Sommen, Peter H. N. de With

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109501Y (2019) https://doi.org/10.1117/12.2508244
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Volumetric laser endomicroscopy (VLE) is an advanced imaging system offering a promising solution for the detection of early Barrett’s esophagus (BE) neoplasia. BE is a known precursor lesion for esophageal adenocarcinoma and is often missed during regular endoscopic surveillance of BE patients. VLE provides a circumferential scan of near-microscopic resolution of the esophageal wall up to 3-mm depth, yielding a large amount of data that is hard to interpret in real time. In a preliminary study on an automated analysis system for ex vivo VLE scans, novel quantitative image features were developed for two previously identified clinical VLE features predictive for BE neoplasia, showing promising results. This paper proposes a novel quantitative image feature for a missing third clinical VLE feature. The novel gland-based image feature called “gland statistics” (GS), is compared to several generic image analysis features and the most promising clinically-inspired feature “layer histogram” (LH). All features are evaluated on a clinical, validated data set consisting of 88 non-dysplastic BE and 34 neoplastic in vivo VLE images for eight different widely-used machine learning methods. The new clinically-inspired feature has on average superior classification accuracy (0.84 AUC) compared to the generic image analysis features (0.61 AUC), as well as comparable performance to the LH feature (0.86 AUC). Also, the LH feature achieves superior classification accuracy compared to the generic image analysis features in vivo, confirming previous ex vivo results. Combining the LH and the novel GS features provides even further improvement of the performance (0.88 AUC), showing great promise for the clinical utility of this algorithm to detect early BE neoplasia.

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Thom Scheeve, Maarten R. Struyvenberg, Wouter L. Curvers, Albert J. de Groof, Erik J. Schoon, Jacques J. G. H. M. Bergman, Fons van der Sommen, and Peter H. N. de With "A novel clinical gland feature for detection of early Barrett’s neoplasia using volumetric laser endomicroscopy", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501Y (13 March 2019); https://doi.org/10.1117/12.2508244

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