Anatomical labeling of intracranial arteries with deep learning in patients with cerebrovascular disease - Hilbert, Adam; Rieger, Jana; Madai, Vince I.; Akay, Ela M.; Aydin, Orhun U.; Behland, Jonas; Khalil, Ahmed A.; Galinovic, Ivana; Sobesky, Jan; Fiebach, Jochen; Livne, Michelle; Frey, Dietmar (Frontiers in Neurology, 2022)
Brain arteries are routinely depicted in the clinical setting by various modalities, e.g. time-of-flight magnetic resonance angiography (TOF-MRA). These imaging techniques have great potential for the diagnosis of cerebrovascular disease, disease progression and response to treatment. Currently, however, only qualitative assessment is implemented in clinical applications, relying on visual inspection. While manual or semi-automated approaches for quantification exist, such solutions are impractical in the clinical setting as they are time consuming, involve too many processing steps and/or neglect image intensity information. In this study, we present for the first time a Deep Learning-based solution for anatomical labeling of intracranial arteries that utilizes complete information from 3D TOF-MRA images. We adapted and trained a state-of-the-art multi-scale Unet architecture with 242 cerebrovascular disease patients to distinguish 24 arterial segments. The proposed model utilizes vessel-specific information as well as raw image intensity information, and can thus take tissue characteristics into account. Our method yielded a performance of 0.89 macro F1 and 0.90 balanced class accuracy (bAcc) in labeling aggregated segments and 0.80 macro F1 and 0.83 bAcc in labeling detailed arterial segments on average. In particular, a higher F1 score than 0.75 for most arteries of clinical interest for cerebrovascular disease was achieved, with higher than 0.90 F1 scores in the larger, main arteries. Due to minimal pre-processing, simple usability and fast predictions, our method could be highly applicable in the clinical setting.