Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications-Reference-Cited by-同舟云学术

Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications

Published:2022-06 Issue:6 Volume:26 Page:2680-2692
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Ma Yang¹,Zhang Chaoyi¹^ORCID,Cabezas Mariano²^ORCID,Song Yang³^ORCID,Tang Zihao¹^ORCID,Liu Dongnan¹^ORCID,Cai Weidong¹^ORCID,Barnett Michael²^ORCID,Wang Chenyu²

Affiliation:

1. School of Computer Science, University of Sydney, Camperdown, NSW, Australia

2. Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia

3. School of Computer Science, Engineering, University of New South Wales, Sydney, NSW, Australia

Funder

Australia Medical Research Future Fund

Cooperative Research Centres, Australian Government Department of Industry

Multiple Sclerosis Research Australia

University of Sydney

University of Sydney Nerve Research Foundation

Microsoft Research Asia Fellowship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/9788469/09714822.pdf?arnumber=9714822

Reference102 articles.

1. A Model of Population and Subject (MOPS) Intensities With Application to Multiple Sclerosis Lesion Segmentation

2. An effective method for computerized prediction and segmentation of multiple sclerosis lesions in brain MRI

3. FSL

4. Mult. sclerosis lesion segmentation from brain MRI via fully convolutional neural networks;roy,2018

5. Multi-output decision trees for lesion segmentation in multiple sclerosis;jog;Proc Med Imag Image Process,0

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