TY - GEN
T1 - A Deep-Learning-Based Framework for Automatic Segmentation and Labelling of Intracranial Artery
AU - Lv, Yi
AU - Liao, Weibin
AU - Liu, Wenjin
AU - Chen, Zhensen
AU - Li, Xuesong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Artery Labelling
KW - Deep Learning
KW - Magnetic Resonance Angiography
KW - Semantic Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85172141582&partnerID=8YFLogxK
U2 - 10.1109/ISBI53787.2023.10230456
DO - 10.1109/ISBI53787.2023.10230456
M3 - Conference contribution
AN - SCOPUS:85172141582
T3 - Proceedings - International Symposium on Biomedical Imaging
BT - 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PB - IEEE Computer Society
T2 - 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Y2 - 18 April 2023 through 21 April 2023
ER -