A Deep-Learning-Based Framework for Automatic Segmentation and Labelling of Intracranial Artery

Yi Lv, Weibin Liao, Wenjin Liu, Zhensen Chen, Xuesong Li

Abstract: Automatic segmentation and labelling of intracranial arteries is important for the clinical diagnosis and research of cerebrovascular disease, but inter-individual differences in intracranial arterial structure pose a serious challenge to automatic processing pipeline. Existing approaches model the arterial labelling task as a centre-line classification problem, neglecting the significance of image-level vessel segmentation and labelling for clinical research. In this paper, we propose a deep learning based automated processing pipeline for joint segmentation and labelling of intracranial arteries, and further again a centre-line vessel type prediction algorithm based on voting model that is capable of obtaining both image-level and centre-line-level arterial labelling results. We used a private dataset containing 167 individual MRA(Magnetic resonance angiography) scans and the public dataset TubeTK for training and testing. The experimental results show that our approach achieves a labelling dice score of 88.3% for 21 intracranial arteries and an average centre-line prediction accuracy of 95%, showing stable and robust results.