Automated classification of optical coherence tomography images of human atrial tissue
| Autor: | Charles C. Marboe, David Tsay, Syed Athar Bin Amir, Yu Gan, Christine P. Hendon |
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| Rok vydání: | 2016 |
| Předmět: |
Male
Pathology medicine.medical_specialty Special Section on Optical Diagnostic and Biophotonic Methods from Bench to Bedside Biomedical Engineering Adipose tissue 030204 cardiovascular system & hematology 01 natural sciences 010309 optics Biomaterials 03 medical and health sciences 0302 clinical medicine Optical coherence tomography 0103 physical sciences Image Processing Computer-Assisted Humans Medicine Segmentation Heart Atria Cardiac imaging Aged Contextual image classification medicine.diagnostic_test business.industry Image segmentation Middle Aged Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials Cardiac Imaging Techniques cardiovascular system Female business Algorithms Tomography Optical Coherence Ex vivo |
| Zdroj: | Journal of Biomedical Optics. 21:101407 |
| ISSN: | 1083-3668 |
| DOI: | 10.1117/1.jbo.21.10.101407 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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