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Journal of Biomedical Optics • Open Access

Automated classification of optical coherence tomography images of human atrial tissue
Author(s): Yu Gan; David Tsay; Syed B. Amir; Charles C. Marboe; Christine P. Hendon

Paper 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 <italic<ex vivo</italic< 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.

Paper Details

Date Published: 29 February 2016
PDF: 11 pages
J. Biomed. Opt. 21(10) 101407 doi: 10.1117/1.JBO.21.10.101407
Published in: Journal of Biomedical Optics Volume 21, Issue 10
Show Author Affiliations
Yu Gan, Columbia Univ. (United States)
David Tsay, New York-Presbyterian Hospital (United States)
Syed B. Amir, Columbia Univ. (United States)
Charles C. Marboe, Columbia Univ. Medical Ctr. (United States)
Christine P. Hendon, Columbia Univ. (United States)


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