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Deep learning approach for the segmentation of aneurysmal ascending aorta

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Abstract

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.

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Acknowledgements

This work was partially supported by a grant (W911NF-18-1-0281) from USA Army Research Office (ARO) to Anthony Yezzi, by a grant (R01-HL-143350) from National Institute of Health (NIH) to Anthony Yezzi, and by a grant (GR-2011-02348129) from the Italian Ministry of Health to Salvatore Pasta.

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Authors and Affiliations

  1. Ri.MED Foundation, Palermo, Italy

    Albert Comelli

  2. Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, Italy

    Albert Comelli, Alessandro Stefano & Viviana Benfante

  3. Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Navdeep Dahiya & Anthony Yezzi

  4. Department of Diagnostic and Therapeutic Services, Radiology Unit, IRCCS-ISMETT, Palermo, Italy

    Giovanni Gentile

  5. Department for the Treatment and Study of Cardiothoracic Diseases and Cardiothoracic Transplantation, IRCCS-ISMETT, Palermo, Italy

    Valentina Agnese, Giuseppe M. Raffa & Michele Pilato

  6. Department of Engineering, University of Palermo, Palermo, Italy

    Giovanni Petrucci & Salvatore Pasta

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  1. Albert Comelli
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Correspondence to Salvatore Pasta.

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Conflict of interest

Albert Comelli declares that he has no conflict of interest. Navdeep Dahiya declares that he has no conflict of interest. Alessandro Stefano declares that he has no conflict of interest. Viviana Benfante declares that she has no conflict of interest. Giovanni Gentile declares that he has no conflict of interest. Valentina Agnese declares that she has no conflict of interest. Giuseppe M Raffa declares that he has no conflict of interest. Michele Pilato declares that he has no conflict of interest. Anthony Yezzi declares that he has no conflict of interest. Giovanni Petrucci declares that he has no conflict of interest. Salvatore Pasta declares that he has no conflict of interest.

Ethical statement

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients for being included in the study. Additional informed consent was obtained from all patients for which identifying information is included in this article.

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Comelli, A., Dahiya, N., Stefano, A. et al. Deep learning approach for the segmentation of aneurysmal ascending aorta. Biomed. Eng. Lett. 11, 15–24 (2021). https://doi.org/10.1007/s13534-020-00179-0

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