Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis

Frontiers in Cardiovascular Medicine

Kathleen Gilbert¹  Conrad Werkhoven¹  Charlène A. Mauger²  Josefine Dam Gade³  Line Sofie Hald³  Avan Suinesiaputra⁴  Markus H. A. Janse⁵  David A. Bluemke⁶  Colin O. Wu⁷  Alistair A. Young⁸  Joao A. C. Lima⁹  Bharath Ambale-Venkatesh⁹ 

[1] Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand;Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand;Department of Biomedical Engineering and Informatics, School of Medicine and Health, Aalborg University, Aalborg, Denmark;Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom;Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands;Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States;Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Baltimore, MD, United States;;Faculty of Life Sciences &Johns Hopkins Medical Center, Baltimore, MD, United States;

关键词: cardiac anatomy;    machine learning;    left ventricle;    MRI;    deep learning;   

DOI  :  10.3389/fcvm.2021.807728

来源: DOAJ

【 摘 要 】

The Multi-Ethnic Study of Atherosclerosis (MESA), begun in 2000, was the first large cohort study to incorporate cardiovascular magnetic resonance (CMR) to study the mechanisms of cardiovascular disease in over 5,000 initially asymptomatic participants, and there is now a wealth of follow-up data over 20 years. However, the imaging technology used to generate the CMR images is no longer in routine use, and methods trained on modern data fail when applied to such legacy datasets. This study aimed to develop a fully automated CMR analysis pipeline that leverages the ability of machine learning algorithms to enable extraction of additional information from such a large-scale legacy dataset, expanding on the original manual analyses. We combined the original study analyses with new annotations to develop a set of automated methods for customizing 3D left ventricular (LV) shape models to each CMR exam and build a statistical shape atlas. We trained VGGNet convolutional neural networks using a transfer learning sequence between two-chamber, four-chamber, and short-axis MRI views to detect landmarks. A U-Net architecture was used to detect the endocardial and epicardial boundaries in short-axis images. The landmark detection network accurately predicted mitral valve and right ventricular insertion points with average error distance <2.5 mm. The agreement of the network with two observers was excellent (intraclass correlation coefficient >0.9). The segmentation network produced average Dice score of 0.9 for both myocardium and LV cavity. Differences between the manual and automated analyses were small, i.e., <1.0 ± 2.6 mL/m2 for indexed LV volume, 3.0 ± 6.4 g/m2 for indexed LV mass, and 0.6 ± 3.3% for ejection fraction. In an independent atlas validation dataset, the LV atlas built from the fully automated pipeline showed similar statistical relationships to an atlas built from the manual analysis. Hence, the proposed pipeline is not only a promising framework to automatically assess additional measures of ventricular function, but also to study relationships between cardiac morphologies and future cardiac events, in a large-scale population study.

【 授权许可】

Unknown