Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms-Reference-Cited by-同舟云学术

Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms

Published:2019-01 Issue:1 Volume:290 Page:187-194
ISSN:0033-8419
Container-title:Radiology
language:en
Short-container-title:Radiology

Author:

Ueda Daiju¹^ORCID,Yamamoto Akira¹,Nishimori Masataka¹,Shimono Taro¹,Doishita Satoshi¹,Shimazaki Akitoshi¹,Katayama Yutaka¹,Fukumoto Shinya¹,Choppin Antoine¹,Shimahara Yuki¹^ORCID,Miki Yukio¹

Affiliation:

1. From the Department of Diagnostic and Interventional Radiology (D.U., A.Y., T.S., S.D., A.S., Y.M.) and Department of Premier Preventive Medicine (S.F.), Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; LPixel, Tokyo, Japan (M.N., A.C., Y.S.); and Department of Radiology, Osaka City University Hospital, Osaka, Japan (Y.K.).

Publisher

Radiological Society of North America (RSNA)

Subject

Radiology Nuclear Medicine and imaging

Link

http://pubs.rsna.org/doi/pdf/10.1148/radiol.2018180901

Reference20 articles.

1. Subarachnoid haemorrhage

2. The Natural Course of Unruptured Cerebral Aneurysms in a Japanese Cohort

3. A Multinational Comparison of Subarachnoid Hemorrhage Epidemiology in the WHO MONICA Stroke Study

4. Case-Fatality Rates and Functional Outcome After Subarachnoid Hemorrhage

5. Can Noninvasive Imaging Accurately Depict Intracranial Aneurysms? A Systematic Review

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