Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning - 05/06/21

Doi : 10.1016/j.diii.2021.05.004

Nicolas Gogin ^a,⁎ , Mario Viti ^a,^c, Luc Nicodème ^a, Mickaël Ohana ^b, Hugues Talbot ^c, Umit Gencer ^d, Magloire Mekukosokeng ^e, Thomas Caramella ^f, Yann Diascorn ^f, Jean-Yves Airaud ^g, Marc-Samir Guillot ^d, Zoubir Bensalah ^h, Caroline Dam Hieu ^a, Bassam Abdallah ^a, Imad Bousaid ⁱ, Nathalie Lassau ^i,^j, Elie Mousseaux ^d
^a General Electric Healthcare, 78530 Buc, France
^b Service de Radiologie, CHU de Strasbourg, 67000 Strasbourg, France
^c CentraleSupélec, Université Paris-Saclay, CentraleSupélec, Inria, 91192 Gif-sur-Yvette, France
^d Radiology Department, AP–HP, Hôpital Européen Georges Pompidou, Georges Pompidou, Université de Paris, PARCC, INSERM, 75015 Paris, France
^e Centre Hospitalier de Douai, 59507 Douai, France
^f Institut Arnault Tzanck, 06123 Saint-Laurent-du-Var, France
^g Department of Radiology, Polyclinique Inkermann, 79000 Niort, France
^h Department of Radiology, Centre Hospitalier de Perpignan, 66000 Perpignan, France
ⁱ Imaging Department, Gustave-Roussy, Université Paris-Saclay, 94076 Villejuif, France
^j Biomaps, UMR 1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94076 Villejuif, France

^⁎Corresponding author.

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Highlights

•	A three-dimensional (3D) deep learning-based model can be trained effectively to compute automatically the coronary artery calcium score from a CT examination.
•	The proposed model is based on a U-Net architecture designed for 3D segmentation of coronary artery calcifications.
•	It would be useful to test and validate this method on a wider range of CT acquisitions, including non-ECG-gated ones, performed for routine examinations for lung disease.

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Abstract

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.

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Keywords : Tomography, X-ray computed, Deep learning, Coronary artery disease, Convolutional neural networks (CNN)

Abbreviations : 2D, 3D, AI, CAD, CAC, C-index, CNN, CT, ECG, FCNN, HU, ICC, SFR