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A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography

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Abstract

Feasibility assessment and planning of thoracic endovascular aortic repair (TEVAR) require computed tomography (CT)-based analysis of geometric aortic features to identify adequate landing zones (LZs) for endograft deployment. However, no consensus exists on how to take the necessary measurements from CT image data. We trained and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, detects proximal landing zones (PLZs), and quantifies geometric features that are relevant for TEVAR planning. For 465 CT scans, the thoracic aorta and pulmonary arteries were manually segmented; 395 randomly selected scans with the corresponding ground truth segmentations were used to train a CNN with a 3D U-Net architecture. The remaining 70 scans were used for testing. The trained CNN was embedded within computational geometry processing pipeline which provides aortic metrics of interest for TEVAR planning. The resulting metrics included aortic arch centerline radius of curvature, proximal landing zones (PLZs) maximum diameters, angulation, and tortuosity. These parameters were statistically analyzed to compare standard arches vs. arches with a common origin of the innominate and left carotid artery (CILCA). The trained CNN yielded a mean Dice score of 0.95 and was able to generalize to 9 pathological cases of thoracic aortic aneurysm, providing accurate segmentations. CILCA arches were characterized by significantly greater angulation (p = 0.015) and tortuosity (p = 0.048) in PLZ 3 vs. standard arches. For both arch configurations, comparisons among PLZs revealed statistically significant differences in maximum zone diameters (p < 0.0001), angulation (p < 0.0001), and tortuosity (p < 0.0001). Our tool allows clinicians to obtain objective and repeatable PLZs mapping, and a range of automatically derived complex aortic metrics.

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Funding

“This work was supported by “Ricerca Corrente” and 5xmille” grants from IRCCS Policlinico San Donato, a clinical research hospital partially funded by the Italian Ministry of Health.

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Authors and Affiliations

  1. Department of Electronics Information and Bioengineering, Politecnico Di Milano, Milan, Italy

    Simone Saitta, Francesco Sturla, Alessandro Caimi, Alessandra Riva, Maria Chiara Palumbo, Emiliano Votta & Alberto Redaelli

  2. 3D and Computer Simulation Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, Italy

    Francesco Sturla, Alessandra Riva & Emiliano Votta

  3. Clinical Research Unit and Division of Vascular Surgery, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Italy

    Giovanni Nano & Massimiliano M. Marrocco-Trischitta

  4. Department of Translational Medical Sciences, University of Campania “L. Vanvitelli”, Unit of Cardiac Surgery, V. Monaldi Hospital, Naples, Italy

    Alessandro Della Corte

  5. Minimally Invasive Cardiac Surgery Unit, Istituto Clinico Sant’Ambrogio, Milan, Italy

    Mattia Glauber

  6. Department of Radiology, CNR (National Council of Research), Tuscany Region ‘Gabriele Monasterio’ Foundation (FTGM), Massa, Italy

    Dante Chiappino

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  1. Simone Saitta
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Corresponding author

Correspondence to Massimiliano M. Marrocco-Trischitta.

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Ethics Approval

The study was approved by the Ethics Committee of reference (i.e., Ethics Committee of IRCCS San Raffaele (Milan, Italy). This study does not include any experiments on human participants but consists in a post hoc analysis of medical images. Patients informed consent was waived by the Ethics Committee because of the retrospective nature of the study, and the analysis of anonymized images. The study was conducted according to the principles outlined in the Declaration of Helsinki.

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Patients informed consent was waived by the Ethics Committee because of the retrospective nature of the study and the analysis of anonymized images. The study was conducted according to the principles outlined in the Declaration of Helsinki.

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All authors gave their consent for publication.

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The authors declare no competing interests.

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Massimiliano M. Marrocco-Trischitta and Alberto Redaelli are equally contributing principal investigators.

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Saitta, S., Sturla, F., Caimi, A. et al. A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography. J Digit Imaging 35, 226–239 (2022). https://doi.org/10.1007/s10278-021-00535-1

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  • DOI: https://doi.org/10.1007/s10278-021-00535-1

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