Automatic A-line coronary plaque classification using combined deep learning and textural features in intravascular OCT images

Juhwan Lee; Chaitanya Kolluru; Yazan Gharaibeh; David Prabhu; Vladislav N. Zimin; Hiram Bezerra; David Wilson

doi:10.1117/12.2549066

16 March 2020 Automatic A-line coronary plaque classification using combined deep learning and textural features in intravascular OCT images

Juhwan Lee, Chaitanya Kolluru, Yazan Gharaibeh, David Prabhu, Vladislav N. Zimin, Hiram Bezerra, David Wilson

Author Affiliations +

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 1131513 (2020) https://doi.org/10.1117/12.2549066
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

We developed a fully automated method for classifying A-line coronary plaques in intravascular optical coherence tomography images using combined deep learning and textural features. The proposed method was trained on 4,292 images from 48 pullbacks giving 80 manually labeled, volumes of interest. Preprocessing steps including guidewire/shadow removal, lumen boundary detection, pixel shifting, and noise reduction were employed. We built a convolutional neural network to extract the deep learning features from the preprocessed image. Traditional textural features were also extracted and combined with deep learning features. Feature selection was performed using the minimum redundancy maximum relevance method. Combined features were utilized as inputs for a random forest classifier. After classification, conditional random field (CRF) method was used for classification noise cleaning. We determined a sub-feature set with the most predictive power. With CRF noise cleaning, sensitivities/specificities were 82.2%/ 90.8% and 82.4%/89.2% for fibrolipidic and fibrocalcific classes, respectively, with good Dice coefficients. The classification noise cleaning step improved performance metrics by nearly 10-15%. The predicted en face classification maps of entire pullbacks agreed favorably to the manually labeled counterparts. Both assessments suggested that our automated measurements gave clinically relevant results. The proposed method is very promising with regards to both clinical treatment planning and research applications.

Conference Presentation

Citation Download Citation

Juhwan Lee, Chaitanya Kolluru, Yazan Gharaibeh, David Prabhu, Vladislav N. Zimin, Hiram Bezerra, and David Wilson "Automatic A-line coronary plaque classification using combined deep learning and textural features in intravascular OCT images", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 1131513 (16 March 2020); https://doi.org/10.1117/12.2549066

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