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Editorial
. 2023 Mar 14:17:1176625.
doi: 10.3389/fnins.2023.1176625. eCollection 2023.

Editorial: Automatic methods for multiple sclerosis new lesions detection and segmentation

Olivier Commowick  1 , Benoît Combès  1 , Frédéric Cervenansky  2 , Michel Dojat  3
Affiliations
Editorial

Editorial: Automatic methods for multiple sclerosis new lesions detection and segmentation

Olivier Commowick et al. Front Neurosci. .
No abstract available

Keywords: MRI; deep learning; image processing; neurodegenerative diseases; neurology - clinical.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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  • Editorial on the Research Topic Automatic methods for multiple sclerosis new lesions detection and segmentation

References

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    1. Bonacchi R., Filippi M., Rocca M. A. (2022). Role of artificial intelligence in MS clinical practice. Neuroimage Clin. 35, 103065. 10.1016/j.nicl.2022.103065 - DOI - PMC - PubMed
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    1. Carass A., Roy S., Jog A., Cuzzocreo J. L., Magrath E., Gherman A., et al. . (2017). Longitudinal multiple sclerosis lesion segmentation data resource. Data Brief 12, 346–350. 10.1016/j.dib.2017.04.004 - DOI - PMC - PubMed
    1. Commowick O., Kain M., Casey R., Ameli R., Ferré J. C., Kerbrat A., et al. . (2021). Multiple sclerosis lesions segmentation from multiple experts: the MICCAI 2016 challenge dataset. Neuroimage 244, 118589. 10.1016/j.neuroimage.2021.118589 - DOI - PubMed

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