Automated classification of optical coherence tomography images of human atrial tissue

Yu Gan; David Tsay; Syed B. Amir; Charles C. Marboe; Christine P. Hendon

doi:10.1117/1.JBO.21.10.101407

29 February 2016 Automated classification of optical coherence tomography images of human atrial tissue

Yu Gan, David Tsay, Syed B. Amir, Charles C. Marboe, Christine P. Hendon

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Journal of Biomedical Optics, Vol. 21, Issue 10, 101407 (February 2016). https://doi.org/10.1117/1.JBO.21.10.101407

Abstract

Tissue composition of the atria plays a critical role in the pathology of cardiovascular disease, tissue remodeling, and arrhythmogenic substrates. Optical coherence tomography (OCT) has the ability to capture the tissue composition information of the human atria. In this study, we developed a region-based automated method to classify tissue compositions within human atria samples within OCT images. We segmented regional information without prior information about the tissue architecture and subsequently extracted features within each segmented region. A relevance vector machine model was used to perform automated classification. Segmentation of human atrial ex vivo datasets was correlated with trichrome histology and our classification algorithm had an average accuracy of 80.41% for identifying adipose, myocardium, fibrotic myocardium, and collagen tissue compositions.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Yu Gan, David Tsay, Syed B. Amir, Charles C. Marboe, and Christine P. Hendon "Automated classification of optical coherence tomography images of human atrial tissue," Journal of Biomedical Optics 21(10), 101407 (29 February 2016). https://doi.org/10.1117/1.JBO.21.10.101407

Published: 29 February 2016

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