Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning.

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

https://www.ncbi.nlm.nih.gov/pubmed/34099435

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;: