Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning.
Fiche publication
Date publication
juin 2021
Journal
Diagnostic and interventional imaging
Auteurs
Membres identifiés du Cancéropôle Est :
Pr OHANA Mickaël
Tous les auteurs :
Gogin N, Viti M, Nicodème L, Ohana M, Talbot H, Gencer U, Mekukosokeng M, Caramella T, Diascorn Y, Airaud JY, Guillot MS, Bensalah Z, Dam Hieu C, Abdallah B, Bousaid I, Lassau N, Mousseaux E
Lien Pubmed
Résumé
The purpose of this study was to develop and evaluate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from unenhanced electrocardiography (ECG)-gated computed tomography (CT) cardiac volume acquisitions by using convolutional neural networks (CNN).
Mots clés
Convolutional neural networks (CNN), Coronary artery disease, Deep learning, Tomography, X-ray computed
Référence
Diagn Interv Imaging. 2021 Jun 4;: