The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria
Polman, C. H., Reingold, S. C., Banwell, B., Clanet, M., Cohen, J. A., Filippi, M., Fujihara, K., Havrdova, E., Hutchinson, M., Kappos, L., et al. (2011) · Annals of Neurology · doi:10.1002/ana.22366 · Cited by 9,802
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
Fisniku, L. K., Brex, P. A., Altmann, D. R., Miszkiel, K. A., Benton, C. E., Lanyon, R., Thompson, A. J., & Miller, D. H. (2008) · Brain · doi:10.1093/brain/awm329 · Cited by 897
Diversity of decision-making models and the measurement of interrater agreement
Uebersax, J. S. (1987) · Psychological Bulletin · doi:10.1037/0033-2909.101.1.140 · Cited by 254 · cited 2×
“8 as “almost perfect” (Landis and Koch, 1977) while others emphasize that “conventional interpretative guidelines” may be misleading as DC values “obtained from samples with different base rates may not be comparable” (Uebersax, 1987).”
BackgroundInfluential
Research works printed but not linked: 44
- Akselrod-Ballin, 2009, Automatic segmentation and classification of multiple sclerosis in multichannel MRI, IEEE Trans Biomed Eng, 56, 2461, 10.1109/TBME.2008.926671 · doi:10.1109/tbme.2008.926671
- Anbeek, 2004, Probabilistic segmentation of white matter lesions in MR imaging, Neuroimage, 21, 1037, 10.1016/j.neuroimage.2003.10.012 · DOI · Paper page
- Ashburner, 2005, Unified segmentation, Neuroimage, 26, 839, 10.1016/j.neuroimage.2005.02.018 · DOI · Paper page
- Bagnato, 2003, Evolution of T1 black holes in patients with multiple sclerosis imaged monthly for 4 years, Brain, 126, 1782, 10.1093/brain/awg182 · DOI · Paper page
- Bakshi, 2001, Fluid-attenuated inversion recovery magnetic resonance imaging detects cortical and juxtacortical multiple sclerosis lesions, Arch. Neurol., 58, 742, 10.1001/archneur.58.5.742 · DOI · Paper page
- Bartko, 1991, Measurement and reliability: statistical thinking considerations, Schizophr. Bull., 17, 483, 10.1093/schbul/17.3.483 · DOI · Paper page
- Bendfeldt, 2010, Spatiotemporal distribution pattern of white matter lesion volumes and their association with regional grey matter volume reductions in relapsing–remitting multiple sclerosis, Hum. Brain Mapp., 31, 1542, 10.1002/hbm.20951 · DOI
- Chard, 2002, Brain atrophy in clinically early relapsing–remitting multiple sclerosis, Brain, 125, 327, 10.1093/brain/awf025 · DOI · Paper page
- Compston, 2008, Multiple sclerosis, Lancet, 372, 1502, 10.1016/S0140-6736(08)61620-7 · DOI · Paper page
- Confavreux, 2008, The clinical epidemiology of multiple sclerosis, Neuroimaging Clin. N. Am., 18, 589, 10.1016/j.nic.2008.09.002 · DOI
- Dice, 1945, Measures of the amount of ecologic association between species, Ecology, 26, 297, 10.2307/1932409 · DOI · Paper page
- Ebers, 1998, Randomised double-blind placebo-controlled study of interferon beta-1a in relapsing/remitting multiple sclerosis. PRISMS (Prevention of Relapses and Disability by Interferon beta-1a Subcutaneously in Multiple Sclerosis) Study Group, Lancet, 352, 1498, 10.1016/S0140-6736(98)03334-0 · DOI · Paper page
- Ferrari, 2003, Segmentation of multiple sclerosis lesions using support vector machines, Proceedings of SPIE, 5032, 16, 10.1117/12.481377 · DOI
- Filippi, 1996, Quantitative assessment of MRI lesion load in multiple sclerosis, a comparison of conventional spin-echo with fast fluid-attenuated inversion recovery, Brain, 119, 1349, 10.1093/brain/119.4.1349 · DOI
- Fisher, 2008, Gray matter atrophy in multiple sclerosis: a longitudinal study, Ann. Neurol., 64, 255, 10.1002/ana.21436 · DOI · Paper page
- Freifeld, 2009, Multiple sclerosis lesion detection using constrained GMM and curve evolution, Int J Biomed Imaging, 2009, 715124, 10.1155/2009/715124 · DOI
- Garcia-Lorenzo, 2009, Multiple sclerosis lesion segmentation using an automatic multimodal graph cuts, Med Image Comput Comput Assist Interv, 12, 584
- Geremia, 2010, Spatial decision forests for MS lesion segmentation in multi-channel MR images, Med Image Comput Comput Assist Interv, 13, 111
- Hammersley, 1971, Markov fields on finite graphs and lattices
- Herskovits, 2008, Automated Bayesian segmentation of microvascular white-matter lesions in the ACCORD-MIND study, Adv. Med. Sci., 53, 182, 10.2478/v10039-008-0039-3 · DOI
- Jacobs, 2000, Intramuscular interferon beta-1a therapy initiated during a first demyelinating event in multiple sclerosis, CHAMPS Study Group, N. Engl. J. Med., 343, 898, 10.1056/NEJM200009283431301 · DOI · Paper page
- Kappos, 1998, Placebo-controlled multicentre randomised trial of interferon beta-1b in treatment of secondary progressive multiple sclerosis. European Study Group on interferon beta-1b in secondary progressive MS, Lancet, 352, 1491, 10.1016/S0140-6736(98)10039-9 · DOI · Paper page
- Khayati, 2008, Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model, Comput. Biol. Med., 38, 379, 10.1016/j.compbiomed.2007.12.005 · doi:10.1016/j.compbiomed.2007.12.005
- Li, 2006, MRI T2 lesion burden in multiple sclerosis: a plateauing relationship with clinical disability, Neurology, 66, 1384, 10.1212/01.wnl.0000210506.00078.5c · DOI
- Li, 2005, Mixture segmentation of multispectral MR brain images for multiple sclerosis, Journal of Systemics, Cybernetics and Informatics, 3, 65
- Lüders, 2009, Why sex matters: brain size independent differences in gray matter distributions between men and women, J. Neurosci., 29, 14265, 10.1523/JNEUROSCI.2261-09.2009 · DOI · Paper page
- Neema, 2009, Normal findings on brain fluid-attenuated inversion recovery MR images at 3T, AJNR Am. J. Neuroradiol., 30, 911, 10.3174/ajnr.A1514 · DOI
- Noseworthy, 2000, Multiple sclerosis, N. Engl. J. Med., 343, 938, 10.1056/NEJM200009283431307 · DOI · Paper page
- Rajapakse, 1997, Statistical approach to segmentation of single-channel cerebral MR images, IEEE Trans. Med. Imaging, 16, 176, 10.1109/42.563663 · DOI · Paper page
- Sahraian, 2010, Black holes in multiple sclerosis: definition, evolution, and clinical correlations, Acta Neurol. Scand., 122, 1, 10.1111/j.1600-0404.2009.01221.x · DOI
- Stevenson, 1997, Imaging of the spinal cord and brain in multiple sclerosis: a comparative study between fast FLAIR and fast spin echo, J. Neurol., 244, 119, 10.1007/s004150050060 · DOI
- Tohka, 2004, Fast and robust parameter estimation for statistical partial volume models in brain MRI, Neuroimage, 23, 84, 10.1016/j.neuroimage.2004.05.007 · DOI · Paper page
- Van Leemput, 2001, Automated segmentation of multiple sclerosis lesions by model outlier detection, IEEE Trans. Med. Imaging, 20, 677, 10.1109/42.938237 · DOI · Paper page
- Wattjes, 2009, High field MRI in the diagnosis of multiple sclerosis: high field-high yield?, Neuroradiology, 51, 279, 10.1007/s00234-009-0512-0 · DOI
- Wattjes, 2006, Higher sensitivity in the detection of inflammatory brain lesions in patients with clinically isolated syndromes suggestive of multiple sclerosis using high field MRI: an intraindividual comparison of 1.5T with 3.0T, Eur. Radiol., 16, 2067, 10.1007/s00330-006-0195-4 · DOI
- Wattjes, 2006, Imaging of inflammatory lesions at 3.0Tesla in patients with clinically isolated syndromes suggestive of multiple sclerosis: a comparison of fluid-attenuated inversion recovery with T2 turbo spin-echo, Eur. Radiol., 16, 1494, 10.1007/s00330-005-0082-4 · DOI
- Weiner, 2009, The challenge of multiple sclerosis: how do we cure a chronic heterogeneous disease?, Ann. Neurol., 65, 239, 10.1002/ana.21640 · DOI · Paper page
- Wels, 2008, Fully automated segmentation of multiple sclerosis lesions in multispectral MRI, Pattern Recognition and Image Analysis, 18, 347, 10.1134/S1054661808020235 · DOI
- Winkler, G., 2003. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. A Mathematical Introduction. Springer-Verlag, 2nd Edition. · DOI
- Woo, 2006, Detection of simulated multiple sclerosis lesions on T2-weighted and FLAIR images of the brain: observer performance, Radiology, 241, 206, 10.1148/radiol.2411050792 · DOI
- Woolrich, 2005, Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data, IEEE Trans. Med. Imaging, 24, 1, 10.1109/TMI.2004.836545 · DOI · Paper page
- Wu, 2006, Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI, Neuroimage, 32, 1205, 10.1016/j.neuroimage.2006.04.211 · DOI
- Zhang, 2001, Segmentation of brain MR images through a hidden Markov random field model and the expectation–maximization algorithm, IEEE Trans. Med. Imaging, 20, 45, 10.1109/42.906424 · DOI · Paper page
- Zijdenbos, 1994, Morphometric analysis of white-matter lesions in MR-images — method and validation, IEEE Trans. Med. Imaging, 13, 716, 10.1109/42.363096 · DOI · Paper page
Cited by
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498
CSF A β 42/A β 40 and A β 42/A β 38 ratios: better diagnostic markers of Alzheimer disease
Janelidze, S., Zetterberg, H., Mattsson, N., Palmqvist, S., Vanderstichele, H., Lindberg, O., van Westen, D., Stomrud, E., Minthon, L., Blennow, K., et al. (2016) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.274 · Cited by 456
Brosch, T., Tang, L. Y. W., Yoo, Y., Li, D. K. B., Traboulsee, A., & Tam, R. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528821 · Cited by 460
on behalf of the MAGNIMS study group, Rovira, À., Wattjes, M. P., Tintoré, M., Tur, C., Yousry, T. A., Sormani, M. P., De Stefano, N., Filippi, M., Auger, C., et al. (2015) · Nature Reviews Neurology · doi:10.1038/nrneurol.2015.106 · Cited by 423
Inflammatory intrathecal profiles and cortical damage in multiple sclerosis
Magliozzi, R., Howell, O. W., Nicholas, R., Cruciani, C., Castellaro, M., Romualdi, C., Rossi, S., Pitteri, M., Benedetti, M. D., Gajofatto, A., et al. (2018) · Annals of Neurology · doi:10.1002/ana.25197 · Cited by 298
Metadata
- Authors
- Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., Hoshi, M., Ilg, R., Schmid, V. J., Zimmer, C., Hemmer, B., & Mühlau, M.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 59
- Issue
- 4
- Pages
- 3774-3783
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2102848905
- Cited by
- 1,223
- References
- 5
- FWCI
- 14.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Funders
- EMD Serono
- Bundesministerium für Bildung und Forschung (01GI0917, 01EV0709)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. https://app.cochat.ai/papers/10.1016/j.neuroimage.2011.11.032
- The paper
- Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., Hoshi, M., Ilg, R., Schmid, V. J., Zimmer, C., Hemmer, B., & Mühlau, M. (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage. https://doi.org/10.1016/j.neuroimage.2011.11.032
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.