High-throughput 3DRA segmentation of brain vasculature and aneurysms using deep learning.

Journal: Computer methods and programs in biomedicine
PMID: 36709557

Abstract

BACKGROUND AND OBJECTIVES: Automatic segmentation of the cerebral vasculature and aneurysms facilitates incidental detection of aneurysms. The assessment of aneurysm rupture risk assists with pre-operative treatment planning and enables in-silico investigation of cerebral hemodynamics within and in the vicinity of aneurysms. However, ensuring precise and robust segmentation of cerebral vessels and aneurysms in neuroimaging modalities such as three-dimensional rotational angiography (3DRA) is challenging. The vasculature constitutes a small proportion of the image volume, resulting in a large class imbalance (relative to surrounding brain tissue). Additionally, aneurysms and vessels have similar image/appearance characteristics, making it challenging to distinguish the aneurysm sac from the vessel lumen.

Authors

  • Fengming Lin
    Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), The University of Leeds, Leeds LS2 9JT, UK.
  • Yan Xia
    Radiological Sciences Lab, Stanford University, 94305, CA, USA.
  • Shuang Song
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Nishant Ravikumar
    Pattern Recognition Lab, Friedrich-Alexander University, Erlangen-Nürnberg, Germany.
  • Alejandro F Frangi
    Information and Communication Technologies Department, Universitat Pompeu Fabra, Barcelona, Spain; Department of Mechanical Engineering, The University of Sheffield, United Kingdom.

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