Geometric Deep Learning Using Vascular Surface Meshes for Modality-Independent Unruptured Intracranial Aneurysm Detection.

Journal: IEEE transactions on medical imaging
Published Date: Oct 27, 2023

Abstract

Early detection of unruptured intracranial aneurysms (UIAs) enables better rupture risk and preventative treatment assessment. UIAs are usually diagnosed on Time-of-Flight Magnetic Resonance Angiographs (TOF-MRA) or contrast-enhanced Computed Tomography Angiographs (CTA). Various automatic voxel-based deep learning UIA detection methods have been developed, but these are limited to a single modality. We propose a modality-independent UIA detection method using a geometric deep learning model with high resolution surface meshes of brain vessels. A mesh convolutional neural network with ResU-Net style architecture was used. UIA detection performance was investigated with different input and pooling mesh resolutions, and including additional edge input features (shape index and curvedness). Both a higher resolution mesh (15,000 edges) and additional curvature edge features improved performance (average sensitivity: 65.6%, false positive count/image (FPC/image): 1.61). UIAs were detected in an independent TOF-MRA test set and a CTA test set with average sensitivity of 52.0% and 48.3% and average FPC/image of 1.04 and 1.05 respectively. We provide modality-independent UIA detection using a deep-learning vascular surface mesh model with comparable performance to state-of-the-art UIA detection methods.

Authors

  • Kimberley M Timmins
  • Irene C van der Schaaf
  • Iris N Vos
  • Ynte M Ruigrok
  • Birgitta K Velthuis
    From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam University Medical Center, University of Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (N.L.); Department of Cardiology, Meander Medical Center, Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn (J.G.T., J.J.C.).
  • Hugo J Kuijf
    Image Sciences Institute, University Medical Center Utrecht and Utrecht University, The Netherlands.

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