3D Automatic Segmentation of Aortic Computed Tomography Angiography Combining Multi-View 2D Convolutional Neural Networks.

Journal: Cardiovascular engineering and technology
PMID: 32783134

Abstract

PURPOSE: The quantitative analysis of contrast-enhanced Computed Tomography Angiography (CTA) is essential to assess aortic anatomy, identify pathologies, and perform preoperative planning in vascular surgery. To overcome the limitations given by manual and semi-automatic segmentation tools, we apply a deep learning-based pipeline to automatically segment the CTA scans of the aortic lumen, from the ascending aorta to the iliac arteries, accounting for 3D spatial coherence.

Authors

  • Alice Fantazzini
    Department of Experimental Medicine, University of Genoa, Via Leon Battista Alberti, 2, 16132, Genoa, Italy. alice.fantazzini@edu.unige.it.
  • Mario Esposito
    Camelot Biomedical Systems S.r.l, Via Al Ponte Reale, 2, 16124, Genoa, Italy.
  • Alice Finotello
    Department of Integrated Surgical and Diagnostic Sciences, University of Genoa, Genoa, Italy.
  • Ferdinando Auricchio
    Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy.
  • Bianca Pane
    Vascular and Endovascular Surgery Unit, IRCCS Ospedale Policlinico San Martino, University of Genoa, Genoa, Italy.
  • Curzio Basso
    Camelot Biomedical Systems S.r.l, Via Al Ponte Reale, 2, 16124, Genoa, Italy.
  • Giovanni Spinella
    Vascular and Endovascular Surgery Unit, IRCCS Ospedale Policlinico San Martino, University of Genoa, Genoa, Italy.
  • Michele Conti
    Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy.

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