Automated tissue classification of intracardiac optical coherence tomography images
(Conference Presentation)

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

doi:10.1117/12.2212796

26 April 2016 Automated tissue classification of intracardiac optical coherence tomography images (Conference Presentation)

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

Author Affiliations +

Proceedings Volume 9697, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XX; 96970C (2016) https://doi.org/10.1117/12.2212796
Event: SPIE BiOS, 2016, San Francisco, California, United States

Abstract

Remodeling of the myocardium is associated with increased risk of arrhythmia and heart failure. Our objective is to automatically identify regions of fibrotic myocardium, dense collagen, and adipose tissue, which can serve as a way to guide radiofrequency ablation therapy or endomyocardial biopsies. Using computer vision and machine learning, we present an automated algorithm to classify tissue compositions from cardiac optical coherence tomography (OCT) images. Three dimensional OCT volumes were obtained from 15 human hearts ex vivo within 48 hours of donor death (source, NDRI). We first segmented B-scans using a graph searching method. We estimated the boundary of each region by minimizing a cost function, which consisted of intensity, gradient, and contour smoothness. Then, features, including texture analysis, optical properties, and statistics of high moments, were extracted. We used a statistical model, relevance vector machine, and trained this model with abovementioned features to classify tissue compositions. To validate our method, we applied our algorithm to 77 volumes. The datasets for validation were manually segmented and classified by two investigators who were blind to our algorithm results and identified the tissues based on trichrome histology and pathology. The difference between automated and manual segmentation was 51.78 ± 50.96 μm. Experiments showed that the attenuation coefficients of dense collagen were significantly different from other tissue types (P < 0.05, ANOVA). Importantly, myocardial fibrosis tissues were different from normal myocardium in entropy and kurtosis. The tissue types were classified with an accuracy of 84%. The results show good agreements with histology.

Conference Presentation

Citation Download Citation

Yu Gan, David Tsay, Syed B. Amir, Charles C. Marboe, and Christine P. Hendon "Automated tissue classification of intracardiac optical coherence tomography images (Conference Presentation)", Proc. SPIE 9697, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XX, 96970C (26 April 2016); https://doi.org/10.1117/12.2212796

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
PRESENTATION

WATCH
PRESENTATION SAVE TO MY LIBRARY

GET CITATION

KEYWORDS

Tissues

Optical coherence tomography

Tissue optics

Image segmentation

Collagen

Heart

Image classification

Show All Keywords

Keywords/Phrases

Search In:

Publication Years