Original Research
Identification and Quantification of Cardiovascular Structures From CCTA: An End-to-End, Rapid, Pixel-Wise, Deep-Learning Method

https://doi.org/10.1016/j.jcmg.2019.08.025Get rights and content
Under an Elsevier user license
open archive

Abstract

Objectives

This study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and quantification.

Background

Segmentation of cardiac structures from coronary computed tomography angiography (CCTA) images is laborious. We designed an end-to-end deep-learning solution.

Methods

Scans were obtained from multicenter registries of 166 patients who underwent clinically indicated CCTA. Left ventricular volume (LVV) and right ventricular volume (RVV), left atrial volume (LAV) and right atrial volume (RAV), and left ventricular myocardial mass (LVM) were manually annotated as ground truth. A U-Net−inspired, deep-learning model was trained, validated, and tested in a 70:20:10 split.

Results

Mean age was 61.1 ± 8.4 years, and 49% were women. A combined overall median Dice score of 0.9246 (interquartile range: 0.8870 to 0.9475) was achieved. The median Dice scores for LVV, RVV, LAV, RAV, and LVM were 0.938 (interquartile range: 0.887 to 0.958), 0.927 (interquartile range: 0.916 to 0.946), 0.934 (interquartile range: 0.899 to 0.950), 0.915 (interquartile range: 0.890 to 0.920), and 0.920 (interquartile range: 0.811 to 0.944), respectively. Model prediction correlated and agreed well with manual annotation for LVV (r = 0.98), RVV (r = 0.97), LAV (r = 0.78), RAV (r = 0.97), and LVM (r = 0.94) (p < 0.05 for all). Mean difference and limits of agreement for LVV, RVV, LAV, RAV, and LVM were 1.20 ml (95% CI: −7.12 to 9.51), −0.78 ml (95% CI: −10.08 to 8.52), −3.75 ml (95% CI: −21.53 to 14.03), 0.97 ml (95% CI: −6.14 to 8.09), and 6.41 g (95% CI: −8.71 to 21.52), respectively.

Conclusions

A deep-learning model rapidly segmented and quantified cardiac structures. This was done with high accuracy on a pixel level, with good agreement with manual annotation, facilitating its expansion into areas of research and clinical import.

Key Words

coronary computed tomography angiography
deep learning
quantification

Abbreviations and Acronyms

CCTA
coronary computed tomography angiography
CMR
cardiac magnetic resonance
CVD
cardiovascular disease
LAV
left atrial volume
LVM
left ventricular myocardial mass
LVV
left ventricular volume
RAV
right atrial volume
RVV
right ventricular volume

Cited by (0)

The study was supported by the Dalio Institute of Cardiovascular Imaging. Dr. Lee has been a consultant for Cleerly. Dr. Bax has received speaker fees from Abbott Vascular and Boehringer Ingelheim. Dr. Min has received funding from the Dalio Foundation, National Institutes of Health, and GE Healthcare; has served on the scientific advisory board of Arineta and GE Healthcare; and has an equity interest in Cleerly. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Joseph Schoepf, MD, served as Guest Editor for this paper.

The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the JACC: Cardiovascular Imaging author instructions page.

Mr. Baskaran and Mr. Maliakal contributed equally to the content of this paper.