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

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

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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.

源语言英语
主期刊名2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
出版商IEEE Computer Society
ISBN(电子版)9781665473583
DOI
出版状态已出版 - 2023
活动20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, 哥伦比亚
期限: 18 4月 202321 4月 2023

出版系列

姓名Proceedings - International Symposium on Biomedical Imaging
2023-April
ISSN(印刷版)1945-7928
ISSN(电子版)1945-8452

会议

会议20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
国家/地区哥伦比亚
Cartagena
时期18/04/2321/04/23

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