Deep learning–based automatic segmentation of meningioma from multiparametric MRI for preoperative meningioma differentiation using radiomic features: a multicentre study-Reference-Cited by-同舟云学术

Deep learning–based automatic segmentation of meningioma from multiparametric MRI for preoperative meningioma differentiation using radiomic features: a multicentre study

Published:2022-04-14 Issue:10 Volume:32 Page:7248-7259
ISSN:1432-1084
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Chen Haolin,Li Shuqi,Zhang Youming,Liu Lizhi,Lv Xiaofei,Yi Yongju,Ruan Guangying,Ke Chao,Feng Yanqiu^ORCID

Funder

National Natural Science Foundation of China

Key-Area Research and Development Program of Guangdong Province

National Key Research and Development Program of China

Technology Research and Development Program of Guangdong

Science and Technology Planning Project of Guangzhou City, China

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-022-08749-9.pdf

Reference34 articles.

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2. Whittle IR, Smith C, Navoo P, Collie D (2004) Meningiomas. Lancet 363:1535–1543

3. Chamoun R, Krisht KM, Couldwell WT (2011) Incidental meningiomas. Neurosurg Foc 31:E19

4. Li X, Miao Y, Han L et al (2019) Meningioma grading using conventional MRI histogram analysis based on 3D tumor measurement. Eur J Radiol 110:45–53

5. Modha A, Gutin PH (2005) Diagnosis and treatment of atypical and anaplastic meningiomas: a review. Neurosurgery 57:538–550 discussion 538-550

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