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

The shape and motion of the heart provide essential clues to understanding the mechanisms of cardiovascular disease. With the advent of large-scale cardiac imaging data, statistical atlases become a powerful tool to provide automated and precise quantification of the status of patient-specific heart geometry with respect to reference populations. The Multi-Ethnic Study of Atherosclerosis (MESA), begun in 2000, was the first large cohort study to incorporate cardiovascular MRI in over 5000 participants, and there is now a wealth of follow-up data over 20 years. Building a machine learning based automated analysis is necessary to extract the additional imaging information necessary for expanding original manual analyses. However, machine learning tools trained on MRI datasets with different pulse sequences fail on such legacy datasets. Here, we describe an automated atlas construction pipeline using deep learning methods applied to the legacy cardiac MRI data in MESA. For detection of anatomical cardiac landmark points, a modified VGGNet convolutional neural network architecture was used in conjunction with a transfer learning sequence between two-chamber, four-chamber, and short-axis MRI views. A U-Net architecture was used for detection of the endocardial and epicardial boundaries in short axis images. Both network architectures resulted in good segmentation and landmark detection accuracies compared with inter-observer variations. Statistical relationships with common risk factors were similar between atlases derived from automated vs manual annotations. The automated atlas can be employed in future studies to examine the relationships between cardiac morphology and future events.

Deep Learning-based Automated Aortic Area and Distensibility Assessment: The Multi-Ethnic Study of Atherosclerosis

This study applies convolutional neural network (CNN)-based automatic segmentation and distensibility measurement of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging (PC-cine MRI) within the large MESA cohort with subsequent assessment on an external cohort of thoracic aortic aneurysm (TAA) patients. 2D PC-cine MRI images of the ascending and descending aorta at the pulmonary artery bifurcation from the MESA study were included. Train, validation, and internal test sets consisted of 1123 studies (24282 images), 374 studies (8067 images), and 375 studies (8069 images), respectively. An external test set of TAAs consisted of 37 studies (3224 images). A U-Net based CNN was constructed, and performance was evaluated utilizing dice coefficient (for segmentation) and concordance correlation coefficients (CCC) of aortic geometric parameters by comparing to manual segmentation and parameter estimation. Dice coefficients for aorta segmentation were 97.6% (CI: 97.5%-97.6%) and 93.6% (84.6%-96.7%) on the internal and external test of TAAs, respectively. CCC for comparison of manual and CNN maximum and minimum ascending aortic areas were 0.97 and 0.95, respectively, on the internal test set and 0.997 and 0.995, respectively, for the external test. CCCs for maximum and minimum descending aortic areas were 0.96 and 0. 98, respectively, on the internal test set and 0.93 and 0.93, respectively, on the external test set. We successfully developed and validated a U-Net based ascending and descending aortic segmentation and distensibility quantification model in a large multi-ethnic database and in an external cohort of TAA patients.