Automated classification of coronary atherosclerotic plaque in optical frequency domain imaging based on deep learning. (July 2021)

• Record Type: Journal Article
• Authors: Shibutani, Hiroki; Fujii, Kenichi; Ueda, Daiju; Kawakami, Rika; Imanaka, Takahiro; Kawai, Kenji; Matsumura, Koichiro; Hashimoto, Kenta; Yamamoto, Akira; Hao, Hiroyuki; Hirota, Seiichi; Miki, Yukio; Shiojima, Ichiro
• Is Part Of: Atherosclerosis. Volume 328(2021)
• Page Start: 100
• Page End: 105
• Publication Date: 2021-07
• Subjects: Coronary artery disease -- Optical coherence tomography -- Artificial intelligence -- Deep learning; Arteriosclerosis -- Periodicals; Electronic journals; 616.136
• DOI: 10.1016/j.atherosclerosis.2021.06.003
• Languages: English
• ISSNs: 0021-9150

Abstract

Abstract: Background and aims: We developed a deep learning (DL) model for automated atherosclerotic plaque categorization using optical frequency domain imaging (OFDI) and performed quantitative and visual evaluations. Methods: A total of 1103 histological cross-sections from 45 autopsy hearts were examined to compare the ex vivo OFDI scans. The images were segmented and annotated considering four histological categories: pathological intimal thickening (PIT), fibrous cap atheroma (FA), fibrocalcific plaque (FC), and healed erosion/rupture (HER). The DL model was developed based on pyramid scene parsing network (PSPNet). Given an input image, a convolutional neural network (ResNet50) was used as an encoder to generate feature maps of the last convolutional layer. Results: For the quantitative evaluation, the mean F-score and IoU values, which are used to evaluate how close the predicted results are to the ground truth, were used. The validation and test dataset had F-score and IoU values of 0.63, 0.49, and 0.66, 0.52, respectively. For the section-level diagnostic accuracy, the areas under the receiver-operating characteristic curve produced by the DL model for FC, PIT, FA, and HER were 0.91, 0.85, 0.86, and 0.86, respectively, and were comparable to those of an expert observer. Conclusions: DL semantic segmentation of coronary plaques in OFDI images was used as a tool to automatically categorize atherosclerotic plaques using histological findings as the gold standard. The proposed method can support interventional cardiologists in understanding histological properties of plaques. Graphical abstract: Image 1 Highlights: A deep learning (DL) model for automated classification of optical frequency domain imaging (OFDI) was developed. It can provide accurate categorization of the atherosclerotic coronary plaque type. This model can support interventional cardiologists in catheterization laboratory.