An atlas of classifiers—a machine learning paradigm for brain MRI segmentation-Reference-Cited by-同舟云学术

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An atlas of classifiers—a machine learning paradigm for brain MRI segmentation

Published:2021-07-27 Issue:9 Volume:59 Page:1833-1849
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Gordon Shiri,Kodner Boris,Goldfryd Tal,Sidorov Michael,Goldberger Jacob,Raviv Tammy Riklin^ORCID

Funder

Israel Science Foundation

Ministry of Science and Technology, Israel

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11517-021-02414-x.pdf

Reference48 articles.

1. Akkus Z, Galimzianova A, Hoogi A, Rubin D, Erickson B (2017) Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging 30(4):449–459

2. Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851

3. Bishop CM (2006) Pattern recognition. Mach Learn: 128

4. Blaiotta C, Freund P, Cardoso MJ, Ashburner J (2018) Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction. Neuroimage 166:117–134

5. Brouwer RM, Pol HEH, Schnack HG (2010) Segmentation of MRI brain scans using non-uniform partial volume densities. Neuroimage 49(1):467–477

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