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Ensemble of Deep Convolutional Neural Networks for Classification of Early Barrett’s Neoplasia Using Volumetric Laser Endomicroscopy

1
Department of Electrical Engineering, Video Coding and Architectures, Eindhoven University of Technology, 5612 AZ Eindhoven, Noord-Brabant, The Netherlands
2
Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, Noord-Holland, The Netherlands
3
Department of Gastroenterology and Hepatology, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work and should be considered co-first authors.
Appl. Sci. 2019, 9(11), 2183; https://doi.org/10.3390/app9112183
Received: 29 April 2019 / Revised: 17 May 2019 / Accepted: 21 May 2019 / Published: 28 May 2019
(This article belongs to the Special Issue Optical Coherence Tomography and its Applications)
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Abstract

Barrett’s esopaghagus (BE) is a known precursor of esophageal adenocarcinoma (EAC). Patients with BE undergo regular surveillance to early detect stages of EAC. Volumetric laser endomicroscopy (VLE) is a novel technology incorporating a second-generation form of optical coherence tomography and is capable of imaging the inner tissue layers of the esophagus over a 6 cm length scan. However, interpretation of full VLE scans is still a challenge for human observers. In this work, we train an ensemble of deep convolutional neural networks to detect neoplasia in 45 BE patients, using a dataset of images acquired with VLE in a multi-center study. We achieve an area under the receiver operating characteristic curve (AUC) of 0.96 on the unseen test dataset and we compare our results with previous work done with VLE analysis, where only AUC of 0.90 was achieved via cross-validation on 18 BE patients. Our method for detecting neoplasia in BE patients facilitates future advances on patient treatment and provides clinicians with new assisting solutions to process and better understand VLE data. View Full-Text
Keywords: Barrett’s esophagus; deep learning; volumetric laser endomicroscopy; optical coherence tomography; classification; esophageal adenocarcinoma; glands; machine learning Barrett’s esophagus; deep learning; volumetric laser endomicroscopy; optical coherence tomography; classification; esophageal adenocarcinoma; glands; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Fonollà, R.; Scheeve, T.; Struyvenberg, M.R.; Curvers, W.L.; de Groof, A.J.; van der Sommen, F.; Schoon, E.J.; Bergman, J.J.; de With, P.H. Ensemble of Deep Convolutional Neural Networks for Classification of Early Barrett’s Neoplasia Using Volumetric Laser Endomicroscopy. Appl. Sci. 2019, 9, 2183.

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