Automated classification of optical coherence tomography images of human atrial tissue

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

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

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.

Original languageEnglish
Article number101407
JournalJournal of Biomedical Optics
Volume21
Issue number10
DOIs
StatePublished - 1 Oct 2016

Keywords

  • cardiac imaging
  • classification
  • image processing
  • optical coherence tomography

Fingerprint

Dive into the research topics of 'Automated classification of optical coherence tomography images of human atrial tissue'. Together they form a unique fingerprint.

Cite this