17 March 2023Histology-trained deep learning model for automated coronary plaque composition assessment in combined intravascular ultrasound-optical coherence tomography images
Xingru Huang,1 Retesh Bajaj,1 Natasha Alves-Kotzev,2 Jill Weyers,2 Molly Levine,3 Mohil Garg,3 Soe Maung,1 Hector Garcia Garcia,3 Kit M. Bransby,1 Ryo Torii,4 Rob Krams,1 Anthony Mathur,1 Andreas Baumbach,1 Qianni Zhanghttps://orcid.org/0000-0001-7685-2187,1 Brian Courtney,2 Christos Bourantas1
1Queen Mary Univ. of London (United Kingdom) 2Sunnybrook Research Institute (Canada) 3MedStar Washington Hospital Ctr. (United States) 4Univ. College London (United Kingdom)
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Combined intravascular ultrasound-optical coherence tomography (IVUS-OCT) enables more accurate coronary plaque tissue classification compared to single modality systems. Automated solutions are needed to that take advantage of information from both modalities to speed such analysis. This study aimed to train and validate a deep learning (DL) model for tissue classification in combined IVUS-OCT images. Coronary segments from 8 arteries from cadaveric human hearts were studied with the Novasight Hybrid imaging catheter. IVUS-OCT images were matched with histological sections and tissue types annotated. These regions of interest were used train and test a DL-classifier for plaque composition (949 matched histological and IVUS-OCT frames from 8 patients for training, 306 frames from 2 patients for testing). The accuracy of the classifier for regional classification was 78.8% suggesting that the trained DL-model is capable of accurate tissue type classification in combined IVUS-OCT images.
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Xingru Huang, Retesh Bajaj, Natasha Alves-Kotzev, Jill Weyers, Molly Levine, Mohil Garg, Soe Maung, Hector Garcia Garcia, Kit M. Bransby, Ryo Torii, Rob Krams, Anthony Mathur, Andreas Baumbach, Qianni Zhang, Brian Courtney, Christos Bourantas, "Histology-trained deep learning model for automated coronary plaque composition assessment in combined intravascular ultrasound-optical coherence tomography images," Proc. SPIE PC12355, Diagnostic and Therapeutic Applications of Light in Cardiology 2023, PC123550A (17 March 2023); https://doi.org/10.1117/12.2655562