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Published July 20, 2020 | Version v1
Journal article Open

A deep learning oriented method for automated 3D reconstruction of carotid arterial trees from MR imaging

Creators

  • 1. Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
  • 2. Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110, Ioannina, Greece, and with the Department of Informatics, Ionian University, GR49100, Corfu, Greece
  • 3. Department of Vascular and Endovascular Surgery, Faculty of Medicine, University of Belgrade, Belgrade, Serbia and with the Department of Vascular and Endovascular Surgery, Clinic Center of Serbia, Belgrade, Serbia
  • 4. Clinic and Policlinik for vascular and endovascular Surgery, Klinikum rechts der Isar, TUM, Germany
  • 5. Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110, Ioannina, Greece, and with the Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas (FORTH)

Description

The scope of this paper is to present a new carotid vessel segmentation algorithm implementing the U-net based convolutional neural network architecture. With carotid atherosclerosis being the major cause of stroke in Europe, new methods that can provide more accurate image segmentation of the carotid arterial tree and plaque tissue can help improve early diagnosis, prevention and treatment of carotid disease.  Herein, we present a novel methodology combining the U-net model and morphological active contours in an iterative framework that accurately segments the carotid lumen and outer wall. The method automatically produces a 3D meshed model of the carotid bifurcation and smaller branches, using multispectral MR image series obtained from two clinical centres of the TAXINOMISIS study. As indicated by a validation study, the algorithm succeeds high accuracy (99.1% for lumen area and 92.6% for the perimeter) for lumen segmentation. The proposed algorithm will be used in the TAXINOMISIS study to obtain more accurate 3D vessel models for improved computational fluid dynamics simulations and the development of models of atherosclerotic plaque progression.

Notes

This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 755320, as part of the TAXINOMISIS project.

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Additional details

Funding

TAXINOMISIS – A multidisciplinary approach for the stratification of patients with carotid artery disease 755320
European Commission