Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection.

Journal: European radiology experimental
Published Date: Jun 29, 2023

Abstract

BACKGROUND: Guidelines recommend that aortic dimension measurements in aortic dissection should include the aortic wall. This study aimed to evaluate two-dimensional (2D)- and three-dimensional (3D)-based deep learning approaches for extraction of outer aortic surface in computed tomography angiography (CTA) scans of Stanford type B aortic dissection (TBAD) patients and assess the speed of different whole aorta (WA) segmentation approaches.

Authors

  • Risto Kesävuori
    Department of Radiology, HUS Medical Imaging Center, Helsinki University Hospital and University of Helsinki, FI-00290, Helsinki, Finland. risto.kesavuori@hus.fi.
  • Tuomas Kaseva
    HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland.
  • Eero Salli
    HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland. eero.salli@hus.fi.
  • Peter Raivio
    Department of Cardiac Surgery, Heart and Lung Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
  • Sauli Savolainen
    HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340 (Haartmaninkatu 4), FI-00290, Helsinki, Finland.
  • Marko Kangasniemi
    HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340 (Haartmaninkatu 4), FI-00290, Helsinki, Finland.

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