Deep learning approach for the segmentation of aneurysmal ascending aorta

Albert Comelli, Navdeep Dahiya, Alessandro Stefano, Viviana Benfante, Giovanni Gentile, Valentina Agnese, Giuseppe M. Raffa, Michele Pilato, Anthony Yezzi, Giovanni Petrucci, Salvatore Pasta

Research output: Contribution to journalArticlepeer-review


Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter, but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel image-derived risk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibility and efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNet techniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspid aortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimics software (Materialize NV, Leuven, Belgium), and then used for training of the tested deep learning models. The segmentation performance in terms of accuracy and time inference were compared using several parameters. All deep learning models reported a dice score higher than 88%, suggesting a good agreement between predicted and manual ATAA segmentation. We found that the ENet and UNet are more accurate than ERFNet, with the ENet much faster than UNet. This study demonstrated that deep learning models can rapidly segment and quantify the 3D geometry of ATAAs with high accuracy, thereby facilitating the expansion into clinical workflow of personalized approach to the management of patients with ATAAs.

Original languageEnglish
JournalBiomedical Engineering Letters
Publication statusAccepted/In press - 2020


  • Aneurysm
  • Aorta
  • Aortic valve
  • Deep learning
  • Segmentation

ASJC Scopus subject areas

  • Biomedical Engineering


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