Deep learning with vessel surface meshes for intracranial aneurysm detection

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.