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It is important that unruptured intracranial aneurysms (UIAs) are detected early for rupture risk and treatment assessment. Radiologists usually visually diagnose UIAs on Time-of-Flight Magnetic Resonance Angiographs (TOF-MRAs) or contrast-enhanced Computed Tomography Angiographs (CTAs). Several automatic UIA detection methods using voxel-based deep learning techniques have been developed, but are limited to a single modality. We propose modality-independent UIA detection by deep learning using mesh surface representations of brain vasculature. Vessels from a training set of 90 brain TOF-MRAs with UIAs were automatically segmented and converted to triangular surface meshes. Vertices and corresponding edges on the surface meshes were labelled as either vessel or aneurysm. A mesh convolutional neural network was trained using the labeled vessel surface meshes as input with a weighted cross-entropy loss function. The network was a U-Net style architecture with convolutional and pooling layers, which operates on mesh edges. The trained network predicted edges on vessel surface meshes, which corresponded to UIAs in a test set of 10 TOF-MRAs and a separate test set of 10 CTAs. UIAs were detected in the test MRAs with an average sensitivity of 65% and an average false positive count/scan of 1.8 and in the test CTAs, with a sensitivity of 65% and a false positive count of 4.1. Using vessel surface meshes it is possible to detect UIAs in TOF-MRAs and CTAs with comparable performance to state-of-the-art UIA detection algorithms. This may aid radiologists in automatic UIA detection without requiring the same image modality or protocol for follow-up imaging.
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K. M. Timmins, I. C. van der Schaaf, I. Vos, Y. M. Ruigrok, B. K. Velthuis, H. J. Kuijf, "Deep learning with vessel surface meshes for intracranial aneurysm detection," Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120332D (4 April 2022); https://doi.org/10.1117/12.2610745