Machine Learning Methods for Barret’s and Dysplasia classification from In Vivo Optical Coherence Tomography Images of Human Esophagus

Christos Photiou; George Plastiras; Guillermo Tearney; Costas Pitris

doi:10.1364/OCT.2020.OW2E.4

Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN)
OSA Technical Digest (Optica Publishing Group, 2020),
paper OW2E.4
•https://doi.org/10.1364/OCT.2020.OW2E.4

Machine Learning Methods for Barret’s and Dysplasia classification from In Vivo Optical Coherence Tomography Images of Human Esophagus

Christos Photiou, George Plastiras, Guillermo Tearney, and Costas Pitris

Optical Coherence Tomography 2020

Washington, DC United States
20–23 April 2020
ISBN: 978-1-943580-74-3

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Image Processing, Machine Learning, and Deep Learning Algorithms (OW2E)

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C. Photiou, G. Plastiras, G. Tearney, and C. Pitris, "Machine Learning Methods for Barret’s and Dysplasia classification from In Vivo Optical Coherence Tomography Images of Human Esophagus," in Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN), OSA Technical Digest (Optica Publishing Group, 2020), paper OW2E.4.

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Abstract

Comparison of machine learning methods for classification of esophageal tissues from in vivo OCT images. A neural network approach provided the best discrimination between normal tissue, Barrett’s and dysplasia, using various features, with 87% accuracy.

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