Automated Cerebrovascular Segmentation and Visualization of Intracranial Time-of-Flight Magnetic Resonance Angiography Based on Deep Learning-Reference-Cited by-同舟云学术

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Automated Cerebrovascular Segmentation and Visualization of Intracranial Time-of-Flight Magnetic Resonance Angiography Based on Deep Learning

Published:2024-08-12 Issue: Volume: Page:
ISSN:2948-2933
Container-title:Journal of Imaging Informatics in Medicine
language:en
Short-container-title:J Digit Imaging. Inform. med.

Author:

Min Yuqin,Li Jing,Jia Shouqiang,Li Yuehua,Nie Shengdong^ORCID

Funder

Natural Science Foundation of Shanghai

Key Program of National Natural Science Foundation of China

Science and Technology Innovation Action Plan of Shanghai

Shanghai Key Laboratory of Molecular Imaging

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10278-024-01215-6.pdf

Reference34 articles.

1. MacDonald M E, Frayne R. Cerebrovascular MRI: a review of state-of-the-art approaches, methods and techniques. NMR Biomed 2015; 28(7): 767-791. https://doi.org/10.1002/nbm.3322

2. Mallouhi A, Felber S, Chemelli A, et al. Detection and characterization of intracranial aneurysms with MR angiography: comparison of volume-rendering and maximum-intensity-projection algorithms [J]. American Journal of Roentgenology, 2003, 180(1): 55-64. https://doi.org/10.2214/ajr.180.1.1800055

3. Bullitt E, Zeng D, Gerig G, et al. Vessel tortuosity and brain tumor malignancy: a blinded study1 [J]. Academic radiology, 2005, 12(10): 1232-1240.

4. Fu F, Wei J, Zhang M, et al. Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network [J]. Nature communications, 2020, 11(1): 4829. https://doi.org/10.1038/s41467-020-18606-2

5. Suganyadevi S, Seethalakshmi V, Balasamy K. A review on deep learning in medical image analysis [J]. International Journal of Multimedia Information Retrieval, 2022, 11(1): 19-38.

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