Journal
DIAGNOSTIC AND INTERVENTIONAL IMAGING
Volume 102, Issue 11, Pages 683-690Publisher
ELSEVIER MASSON, CORP OFF
DOI: 10.1016/j.diii.2021.05.004
Keywords
Tomography, X-ray computed; Deep learning; Coronary artery disease; Convolutional neural networks (CNN)
Ask authors/readers for more resources
The study developed and evaluated an algorithm using convolutional neural networks to automatically estimate coronary artery calcium from unenhanced ECG-gated cardiac CT. The final model achieved a C-index of 0.951 on the testing set, demonstrating fast, robust, and accurate performance, especially for calcifications in the testing set.
Purpose: 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). Materials and methods: The method used a set of five CNN with three-dimensional (3D) U-Net architecture trained on a database of 783 CT examinations to detect and segment coronary artery calcifications in a 3D volume. The Agatston score, the conventional CAC scoring, was then computed slice by slice from the resulting segmentation mask and compared to the ground truth manually estimated by radiologists. The quality of the estimation was assessed with the concordance index (C-index) on CAC risk category on a separate testing set of 98 independent CT examinations. Results: The final model yielded a C-index of 0.951 on the testing set. The remaining errors of the method were mainly observed on small-size and/or low-density calcifications, or calcifications located near the mitral valve or ring. Conclusion: The deep learning-based method proposed here to compute automatically the CAC score from unenhanced-ECG-gated cardiac CT is fast, robust and yields accuracy similar to those of other artificial intelligence methods, which could improve workflow efficiency, eliminating the time spent on manually selecting coronary calcifications to compute the Agatston score. (C) 2021 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available