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Proceedings Paper

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

Paper Details

Date Published: 16 March 2020
PDF: 14 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 1131513 (16 March 2020); doi: 10.1117/12.2549066
Show Author Affiliations
Juhwan Lee, Case Western Reserve Univ. (United States)
Chaitanya Kolluru, Case Western Reserve Univ. (United States)
Yazan Gharaibeh, Case Western Reserve Univ. (United States)
David Prabhu, Case Western Reserve Univ. (United States)
Vladislav N. Zimin, Univ. Hospitals Cleveland Medical Ctr. (United States)
Hiram Bezerra, Univ. Hospitals Cleveland Medical Ctr. (United States)
David Wilson, Case Western Reserve Univ. (United States)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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