Cross-Sectional Validation of an Automated Lesion Segmentation Software in Multiple Sclerosis: Comparison with Radiologist Assessments

References

1. 1.↵
   1. Thompson AJ,
   2. Banwell BL,
   3. Barkhof F, et al

   . Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 2018;17:162–73 doi:10.1016/S1474-4422(17)30470-2 pmid:29275977

   CrossRefPubMed
2. 2.↵
   1. Filippi M,
   2. Rocca MA

   . MR imaging of multiple sclerosis. Radiology 2011;259:659–81 doi:10.1148/radiol.11101362 pmid:21602503

   CrossRefPubMedWeb of Science
3. 3.↵
   1. Diaz-Hurtado M,
   2. Martínez-Heras E,
   3. Solana E, et al

   . Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review. Neuroradiology 2022;64:2103–17 doi:10.1007/s00234-022-03019-3 pmid:35864180

   CrossRefPubMed
4. 4.↵
   1. Egger C,
   2. Opfer R,
   3. Wang C, et al

   . MRI FLAIR lesion segmentation in multiple sclerosis: does automated segmentation hold up with manual annotation? Neuroimage Clin 2017;13:264–70 doi:10.1016/j.nicl.2016.11.020 pmid:28018853

   CrossRefPubMed
5. 5.↵
   1. Bonacchi R,
   2. Filippi M,
   3. Rocca MA

   . Role of artificial intelligence in MS clinical practice. Neuroimage Clin 2022;35:103065 doi:10.1016/j.nicl.2022.103065 pmid:35661470

   CrossRefPubMed
6. 6.↵
   1. Fartaria MJ,
   2. Bonnier G,
   3. Roche A, et al

   . Automated detection of white matter and cortical lesions in early stages of multiple sclerosis. J Magn Reson Imaging 2016;43:1445–54 doi:10.1002/jmri.25095 pmid:26606758

   CrossRefPubMed
7. 7.↵
   1. Fartaria MJ,
   2. Todea A,
   3. Kober T, et al

   . Partial volume-aware assessment of multiple sclerosis lesions. Neuroimage Clin 2018;18:245–53 doi:10.1016/j.nicl.2018.01.011 pmid:29868448

   CrossRefPubMed
8. 8.↵
   1. Ayrignac X,
   2. Le Bars E,
   3. Duflos C, et al

   . Serum GFAP in multiple sclerosis: correlation with disease type and MRI markers of disease severity. Sci Rep 2020;10:10923 doi:10.1038/s41598-020-67934-2 pmid:32616916

   CrossRefPubMed
9. 9.↵
   1. Griffanti L,
   2. Zamboni G,
   3. Khan A, et al

   . BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities. Neuroimage 2016;141:191–205 doi:10.1016/j.neuroimage.2016.07.018 pmid:27402600

   CrossRefPubMed
10. 10.↵
    1. Steenwijk MD,
    2. Pouwels PJ,
    3. Daams M, et al

    . Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs). Neuroimage Clin 2013;3:462–69 doi:10.1016/j.nicl.2013.10.003 pmid:24273728

    CrossRefPubMed
11. 11.↵
    1. Wattjes MP,
    2. Ciccarelli O,
    3. Reich DS

    ; North American Imaging in Multiple Sclerosis Cooperative MRI Guidelines Working Group, et al. 2021 MAGNIMS-CMSC-NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol 2021;20:653–70 doi:10.1016/S1474-4422(21)00095-8 pmid:34139157

    CrossRefPubMed
12. 12.↵
    1. Kurtzke JF

    . Rating neurologic impairment in multiple sclerosis. An expanded disability status scale (EDSS) 1983;33:1444–1444 doi:10.1212/WNL.33.11.1444

    CrossRef
13. 13.↵
    1. Wu Y,
    2. Warfield SK,
    3. Tan IL, et al

    . Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI. Neuroimage 2006;32:1205–15 doi:10.1016/j.neuroimage.2006.04.211 pmid:16797188

    CrossRefPubMed
14. 14.↵
    1. Maldjian JA,
    2. Laurienti PJ,
    3. Kraft RA, et al

    . An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 2003;19:1233–39 doi:10.1016/s1053-8119(03)00169-1 pmid:12880848

    CrossRefPubMedWeb of Science
15. 15.↵
    1. Kiryu S,
    2. Akai H,
    3. Yasaka K, et al

    . Clinical impact of deep learning reconstruction in MRI. Radiographics 2023;43:e220133 doi:10.1148/rg.220133 pmid:37200221

    CrossRefPubMed