A deep learning-based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography images

Abdolmanafi, Atefeh, Duong, Luc, Ibrahim, Ragui et Dahdah, Nagib. 2021. « A deep learning-based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography images ». Medical Physics, vol. 48, nº 7. pp. 3511-3524.
Compte des citations dans Scopus : 12.

Prévisualisation

PDF Duong-L-2021-22754.pdf - Version acceptée Licence d'utilisation : Tous les droits réservés aux détenteurs du droit d'auteur. Télécharger (5MB) | Prévisualisation

Résumé

Purpose -- Coronary artery events are mainly associated with atherosclerosis in adult population, which is recognized as accumulation of plaques in arterial wall tissues. Optical Coherence Tomography (OCT) is a light-based imaging system used in cardiology to analyze intracoronary tissue layers and pathological formations including plaque accumulation. This state-of-the-art catheter-based imaging system provides intracoronary cross-sectional images with high resolution of 10-15 µm. But interpretation of the acquired images is operator dependent, which is not only very time-consuming but also highly error prone from one observer to another. An automatic and accurate coronary plaque tagging using OCT image post-processing can contribute to wide adoption of the OCT system and reducing the diagnostic error rate. Method -- In this study, we propose a combination of spatial pyramid pooling module with dilated convolutions for semantic segmentation to extract atherosclerotic tissues regardless of their types and training a sparse auto-encoder to reconstruct the input features and enlarge the training data as well as plaque type characterization in OCT images. Results -- The results demonstrate high precision of the proposed model with reduced computational complexity, which can be appropriate for real-time analysis of OCT images. At each step of the work, measured accuracy, sensitivity, specificity of more than 93% demonstrate high performance of the model. Conclusion -- The main focus of this study is atherosclerotic tissue characterization using OCT imaging. This contributes to wide adoption of the OCT imaging system by providing clinicians with a fully automatic interpretation of various atherosclerotic tissues. Future studies will be focused on analyzing atherosclerotic vulnerable plaques, those coronary plaques which are prone to rupture.

Type de document:	Article publié dans une revue, révisé par les pairs
Professeur:	Professeur Duong, Luc
Affiliation:	Génie logiciel et des technologies de l'information
Date de dépôt:	08 juin 2021 19:04
Dernière modification:	24 mai 2022 14:49
URI:	https://espace2.etsmtl.ca/id/eprint/22754

Actions (Authentification requise)

Dernière vérification avant le dépôt