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Deep Learning-based Automated Aortic Area and Distensibility Assessment: the Multi-Ethnic Study of Atherosclerosis (MESA)


This study details application of deep learning for automatic segmentation of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging for automatic aortic analysis on the large MESA cohort with assessment on an external cohort of thoracic aortic aneurysm (TAA) patients. This study includes images and corresponding analysis of the ascending and descending aorta at the pulmonary artery bifurcation from the MESA study. Train, validation, and internal test sets consisted of 1123 studies (24,282 images), 374 studies (8067 images), and 375 studies (8069 images), respectively. The external test set of TAAs consisted of 37 studies (3224 images). CNN performance was evaluated utilizing a dice coefficient and concordance correlation coefficients (CCC) of geometric parameters. Dice coefficients were as high as 97.55% (CI: 97.47–97.62%) and 93.56% (CI: 84.63–96.68%) on the internal and external test of TAAs, respectively. CCC for maximum and minimum and ascending aortic area were 0.969 and 0.950, respectively, on the internal test set and 0.997 and 0.995, respectively, for the external test. The absolute differences between manual and deep learning segmentations for ascending and descending aortic distensibility were 0.0194 × 10−4 ± 9.67 × 10−4 and 0.002 ± 0.001 mmHg−1, respectively, on the internal test set and 0.44 × 10−4 ± 20.4 × 10−4 and 0.002 ± 0.001 mmHg−1, respectively, on the external test set. We successfully developed a U-Net-based aortic segmentation and analysis algorithm in both MESA and in external cases of TAA.

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The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at


This research was supported by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS).

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Authors and Affiliations



VJ, JL, and BAV conceived and designed the study. VJ, NK, AR, GTT, KB, and ADC performed measurements and manual segmentations. NK and AR provided the ArtFUN software. VJ and BAV performed statistical analysis. VJ, SK, JL, and BAV wrote the manuscript. SK, CW, and DB provided additional assistance drafting the manuscript. All authors revised the manuscript critically for important intellectual content, and all authors read and approved the final version to be published.

Corresponding author

Correspondence to Bharath Ambale-Venkatesh.

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Ethics Approval and Consent to Participate

All participants gave informed consent for the study protocol, which was approved by the institutional review boards of all MESA field centers and the CMR reading center. MESA field center IRB numbers (WFU—IRB00008492, COL—IRB00002973; JHU – 00001656; UMH—IRB00000438; NWU—IRB00005003; UCLA – 00000172).

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All authors have provided consent for publication.

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There is no potential conflict of interest, real or perceived, by the authors. The views expressed in this manuscript are those of the authors and do not necessary represent the view of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.

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Jani, V.P., Kachenoura, N., Redheuil, A. et al. Deep Learning-based Automated Aortic Area and Distensibility Assessment: the Multi-Ethnic Study of Atherosclerosis (MESA). J Digit Imaging 35, 594–604 (2022).

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  • Deep learning
  • U-Net
  • Cardiovascular disease
  • Coronary artery disease
  • Aortic distensibility
  • Aortic aneurysm