The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Open accessAbstract
A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available. Described as nonparametric nonuniform intensity normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. The performance of this method is evaluated using both real and simulated MR data.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Also, the correction for image intensity has been proven to recover most of the image artifacts present in the International Consortium of Brain Mapping MR database (Sled et al., 1998).”
From Self-esteem, locus of control, hippocampal volume, and cortisol regulation in young and old adulthood (2005) · cites this paper as Sled et al., 1998
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
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Pattern Recognition with Fuzzy Objective Function Algorithms
Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881
Talairach, J., Tournoux, P., & Rayport, M. (1988) · Thieme eBooks · Cited by 5,058
Non-Uniform Random Variate Generation
Devroye, L. (1986) · Springer eBooks · doi:10.1007/978-1-4613-8643-8 · Cited by 4,090
Pattern classification and scene analysis
Chien, Y. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100577 · Cited by 3,421
Adaptive segmentation of MRI data
Wells, W. M., Grimson, W. E. L., Kikinis, R., & Jolesz, F. A. (1996) · IEEE Transactions on Medical Imaging · doi:10.1109/42.511747 · Cited by 1,291
Morphometric analysis of white matter lesions in MR images: method and validation
Zijdenbos, A. P., Dawant, B. M., Margolin, R. A., & Palmer, A. C. (1994) · IEEE Transactions on Medical Imaging · doi:10.1109/42.363096 · Cited by 1,268
Self-citationBloch, F., Hansen, W. W., & Packard, M. E. (1946) · Physical Review · doi:10.1103/physrev.69.127 · Cited by 945
Flagged first, then by citations
Research works printed but not linked: 2
- thulborn, 1993, b1 correction maps and apparent water density maps as tools for quantitative functional mri, Soc Magn Reson Med, 1, 347
- zijdenbos, 1995, intensity correction and its effect on measurement variability in the computer-aided analysis of mri, Proc 9th Int Symp Exhibit Computer Assisted Radiology (CAR), 216
Cited by
Sorted by citations
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507
Dynamic mapping of human cortical development during childhood through early adulthood
Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., Nugent, T. F., Herman, D. H., Clasen, L. S., Toga, A. W., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0402680101 · Cited by 5,604
The minimal preprocessing pipelines for the Human Connectome Project
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.127 · Cited by 5,253
Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020) · Radiology · doi:10.1148/radiol.2020191145 · Cited by 4,177
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049
Within-subject template estimation for unbiased longitudinal image analysis
Reuter, M., Schmansky, N. J., Rosas, H. D., & Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.084 · Cited by 2,356
Unbiased average age-appropriate atlases for pediatric studies
Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.033 · Cited by 2,290
Self-citationBrain development in children and adolescents: Insights from anatomical magnetic resonance imaging
Lenroot, R. K., & Giedd, J. N. (2006) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2006.06.001 · Cited by 1,863
Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation
Shaw, P., Eckstrand, K., Sharp, W., Blumenthal, J., Lerch, J. P., Greenstein, D., Clasen, L., Evans, A., Giedd, J., & Rapoport, J. L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707741104 · Cited by 1,819
Self-citationMapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI
Glasser, M. F., & Van Essen, D. C. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.2180-11.2011 · Cited by 1,447
Intellectual ability and cortical development in children and adolescents
Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., Evans, A., Rapoport, J., & Giedd, J. (2006) · Nature · doi:10.1038/nature04513 · Cited by 1,543
Self-citationAllele-specific FKBP5 DNA demethylation mediates gene–childhood trauma interactions
Klengel, T., Mehta, D., Anacker, C., Rex-Haffner, M., Pruessner, J. C., Pariante, C. M., Pace, T. W. W., Mercer, K. B., Mayberg, H. S., Bradley, B., et al. (2013) · Nature Neuroscience · doi:10.1038/nn.3275 · Cited by 1,447
Iturria-Medina, Y., Sotero, R. C., Toussaint, P. J., Mateos-Pérez, J. M., Evans, A. C., Initiative, T. A. D. N., Weiner, M. W., Aisen, P., Petersen, R., Jack, C. R., et al. (2016) · Nature Communications · doi:10.1038/ncomms11934 · Cited by 1,202
Self-citationDynamics of Gray Matter Loss in Alzheimer's Disease
Thompson, P. M., Hayashi, K. M., de Zubicaray, G., Janke, A. L., Rose, S. E., Semple, J., Herman, D., Hong, M. S., Dittmer, S. S., Doddrell, D. M., et al. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-03-00994.2003 · Cited by 1,132
Jack, C. R., Lowe, V. J., Weigand, S. D., Wiste, H. J., Senjem, M. L., Knopman, D. S., Shiung, M. M., Gunter, J. L., Boeve, B. F., Kemp, B. J., et al. (2009) · Brain · doi:10.1093/brain/awp062 · Cited by 1,099
Automated model-based tissue classification of MR images of the brain
Van Leemput, K., Maes, F., Vandermeulen, D., & Suetens, P. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.811270 · Cited by 1,024
Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis
Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968
He, Y., Chen, Z., & Evans, A. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.0141-08.2008 · Cited by 965
Self-citationPezawas, L., Verchinski, B. A., Mattay, V. S., Callicott, J. H., Kolachana, B. S., Straub, R. E., Egan, M. F., Meyer-Lindenberg, A., & Weinberger, D. R. (2004) · The Journal of Neuroscience · doi:10.1523/jneurosci.2680-04.2004 · Cited by 890
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.061 · Cited by 817
LaMontagne, P. J., Benzinger, T. L., Morris, J. C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A. G., et al. (2019) · medRxiv · doi:10.1101/2019.12.13.19014902 · Cited by 779
“…matter and deep gray matter volumetric structures (including hippocampus, amygdala, caudate, putamen, ventricles) 26,27 intensity normalization 35 , tessellation of the gray matter white matter boundary, automated topology correction 25,36 , and surface deformation following intensity…”
Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-González, J., Routier, A., Bottani, S., Dormont, D., Durrleman, S., Burgos, N., & Colliot, O. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101694 · Cited by 809
Tamnes, C. K., Herting, M. M., Goddings, A.-L., Meuwese, R., Blakemore, S.-J., Dahl, R. E., Güroğlu, B., Raznahan, A., Sowell, E. R., Crone, E. A., et al. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3302-16.2017 · Cited by 685
Pruessner, J. C. (2000) · Cerebral Cortex · doi:10.1093/cercor/10.4.433 · Cited by 706
One-Year Brain Atrophy Evident in Healthy Aging
Fjell, A. M., Walhovd, K. B., Fennema-Notestine, C., McEvoy, L. K., Hagler, D. J., Holland, D., Brewer, J. B., & Dale, A. M. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.3252-09.2009 · Cited by 692
Limperopoulos, C., Tworetzky, W., McElhinney, D. B., Newburger, J. W., Brown, D. W., Robertson, R. L., Guizard, N., McGrath, E., Geva, J., Annese, D., et al. (2010) · Circulation · doi:10.1161/circulationaha.109.865568 · Cited by 663
Buss, C., Davis, E. P., Shahbaba, B., Pruessner, J. C., Head, K., & Sandman, C. A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1201295109 · Cited by 652
Teicher, M. H., Anderson, C. M., & Polcari, A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1115396109 · Cited by 639
Effective treatment of chronic low back pain in humans reverses abnormal brain anatomy and function
Seminowicz, D. A., Wideman, T. H., Naso, L., Hatami-Khoroushahi, Z., Fallatah, S., Ware, M. A., Jarzem, P., Bushnell, M. C., Shir, Y., Ouellet, J. A., et al. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.5280-10.2011 · Cited by 622
Mills, K. L., Goddings, A.-L., Herting, M. M., Meuwese, R., Blakemore, S.-J., Crone, E. A., Dahl, R. E., Güroğlu, B., Raznahan, A., Sowell, E. R., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.044 · Cited by 600
Lian, C., Liu, M., Zhang, J., & Shen, D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2889096 · Cited by 544
Storsve, A. B., Fjell, A. M., Tamnes, C. K., Westlye, L. T., Overbye, K., Aasland, H. W., & Walhovd, K. B. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0391-14.2014 · Cited by 542
Deep MRI brain extraction: A 3D convolutional neural network for skull stripping
Kleesiek, J., Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., & Biller, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.024 · Cited by 505
Developmental changes in the structure of the social brain in late childhood and adolescence
Mills, K. L., Lalonde, F., Clasen, L. S., Giedd, J. N., & Blakemore, S.-J. (2014) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nss113 · Cited by 452
Hanson, J. L., Chung, M. K., Avants, B. B., Shirtcliff, E. A., Gee, J. C., Davidson, R. J., & Pollak, S. D. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.0859-10.2010 · Cited by 455
Deistung, A., Schäfer, A., Schweser, F., Biedermann, U., Turner, R., & Reichenbach, J. R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.055 · Cited by 442
Nair, T., Precup, D., Arnold, D. L., & Arbel, T. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101557 · Cited by 451
Decreased interhemispheric functional connectivity in autism
Anderson, J. S., Druzgal, T. J., Froehlich, A., DuBray, M. B., Lange, N., Alexander, A. L., Abildskov, T., Nielsen, J. A., Cariello, A. N., Cooperrider, J. R., et al. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq190 · Cited by 438
Shi, J., Zheng, X., Li, Y., Zhang, Q., & Ying, S. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2655720 · Cited by 444
Family Poverty Affects the Rate of Human Infant Brain Growth
Hanson, J. L., Hair, N., Shen, D. G., Shi, F., Gilmore, J. H., Wolfe, B. L., & Pollak, S. D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0080954 · Cited by 430
Randomized controlled trial of atorvastatin in mild to moderate Alzheimer disease: LEADe
Feldman, H. H., Doody, R. S., Kivipelto, M., Sparks, D. L., Waters, D. D., Jones, R. W., Schwam, E., Schindler, R., Hey-Hadavi, J., DeMicco, D. A., et al. (2010) · Neurology · doi:10.1212/wnl.0b013e3181d6476a · Cited by 438
Networks of anatomical covariance
Evans, A. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.054 · Cited by 429
Self-citationNeuroimaging signatures of frontotemporal dementia genetics: C9ORF72, tau, progranulin and sporadics
Whitwell, J. L., Weigand, S. D., Boeve, B. F., Senjem, M. L., Gunter, J. L., DeJesus-Hernandez, M., Rutherford, N. J., Baker, M., Knopman, D. S., Wszolek, Z. K., et al. (2012) · Brain · doi:10.1093/brain/aws001 · Cited by 397
Cortical thickness and pain sensitivity in zen meditators.
Grant, J. A., Courtemanche, J., Duerden, E. G., Duncan, G. H., & Rainville, P. (2010) · Emotion · doi:10.1037/a0018334 · Cited by 395
Absolute diffusivities define the landscape of white matter degeneration in Alzheimer’s disease
Acosta-Cabronero, J., Williams, G. B., Pengas, G., & Nestor, P. J. (2010) · Brain · doi:10.1093/brain/awp257 · Cited by 393
Federated learning enables big data for rare cancer boundary detection
Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33407-5 · Cited by 394
Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer’s disease
Singh, V., Chertkow, H., Lerch, J. P., Evans, A. C., Dorr, A. E., & Kabani, N. J. (2006) · Brain · doi:10.1093/brain/awl256 · Cited by 386
Self-citationCraft, S., Raman, R., Chow, T. W., Rafii, M. S., Sun, C.-K., Rissman, R. A., Donohue, M. C., Brewer, J. B., Jenkins, C., Harless, K., et al. (2020) · JAMA Neurology · doi:10.1001/jamaneurol.2020.1840 · Cited by 389
Durston, S., Pol, H. E. H., Schnack, H. G., Buitelaar, J. K., Steenhuis, M. P., Minderaa, R. B., Kahn, R. S., & van engeland, H. (2004) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/00004583-200403000-00016 · Cited by 376
Lupien, S. J., Parent, S., Evans, A. C., Tremblay, R. E., Zelazo, P. D., Corbo, V., Pruessner, J. C., & Séguin, J. R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1105371108 · Cited by 379
Self-citationfMRI evidence of compensatory mechanisms in older adults at genetic risk for Alzheimer disease
Bondi, M. W., Houston, W. S., Eyler, L. T., & Brown, G. G. (2005) · Neurology · doi:10.1212/01.wnl.0000150885.00929.7e · Cited by 372
Development of Cortical Surface Area and Gyrification in Attention-Deficit/Hyperactivity Disorder
Shaw, P., Malek, M., Watson, B., Sharp, W., Evans, A., & Greenstein, D. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.01.031 · Cited by 357
Self-citationGray matter differences correlate with spontaneous strategies in a human virtual navigation task
Bohbot, V. D., Lerch, J., Thorndycraft, B., Iaria, G., & Zijdenbos, A. P. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.1763-07.2007 · Cited by 347
Self-citationShen, M. D., Nordahl, C. W., Young, G. S., Wootton-Gorges, S. L., Lee, A., Liston, S. E., Harrington, K. R., Ozonoff, S., & Amaral, D. G. (2013) · Brain · doi:10.1093/brain/awt166 · Cited by 345
Hippocampus and Amygdala Morphology in Attention-Deficit/Hyperactivity Disorder
Plessen, K. J., Bansal, R., Zhu, H., Whiteman, R., Amat, J., Quackenbush, G. A., Martin, L., Durkin, K., Blair, C., Royal, J., et al. (2006) · Archives of General Psychiatry · doi:10.1001/archpsyc.63.7.795 · Cited by 347
Reduced physical activity increases intermuscular adipose tissue in healthy young adults
Manini, T. M., Clark, B. C., Nalls, M. A., Goodpaster, B. H., Ploutz-Snyder, L. L., & Harris, T. B. (2007) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/85.2.377 · Cited by 332
Ducharme, S., Albaugh, M. D., Nguyen, T.-V., Hudziak, J. J., Mateos-Pérez, J. M., Labbe, A., Evans, A. C., & Karama, S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.010 · Cited by 304
Self-citationPruessner, J. C., Baldwin, M. W., Dedovic, K., Renwick, R., Mahani, N. K., Lord, C., Meaney, M., & Lupien, S. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.06.014 · Cited by 336
McEvoy, L. K., Fennema-Notestine, C., Roddey, J. C., Hagler, D. J., Holland, D., Karow, D. S., Pung, C. J., Brewer, J. B., & Dale, A. M. (2009) · Radiology · doi:10.1148/radiol.2511080924 · Cited by 330
Lenroot, R. K., Schmitt, J. E., Ordaz, S. J., Wallace, G. L., Neale, M. C., Lerch, J. P., Kendler, K. S., Evans, A. C., & Giedd, J. N. (2009) · Human Brain Mapping · doi:10.1002/hbm.20494 · Cited by 320
Self-citationCerebellar development and clinical outcome in attention deficit hyperactivity disorder
Mackie, S., Shaw, P., Lenroot, R., Pierson, R., Greenstein, D. K., Nugent, T. F., Sharp, W. S., Giedd, J. N., & Rapoport, J. L. (2007) · American Journal of Psychiatry · doi:10.1176/appi.ajp.164.4.647 · Cited by 333
Shaw, P., Gilliam, M., Liverpool, M., Weddle, C., Malek, M., Sharp, W., Greenstein, D., Evans, A., Rapoport, J., & Giedd, J. (2011) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2010.10030385 · Cited by 322
Self-citationJoshi, S. H., Espinoza, R. T., Pirnia, T., Shi, J., Wang, Y., Ayers, B., Leaver, A., Woods, R. P., & Narr, K. L. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.02.029 · Cited by 320
Delayed cortical development in fetuses with complex congenital heart disease
Clouchoux, C., du Plessis, A. J., Bouyssi-Kobar, M., Tworetzky, W., McElhinney, D. B., Brown, D. W., Gholipour, A., Kudelski, D., Warfield, S. K., McCarter, R. J., et al. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs281 · Cited by 309
Self-citationDecreased Cortical Representation of Genital Somatosensory Field After Childhood Sexual Abuse
Heim, C. M., Mayberg, H. S., Mletzko, T., Nemeroff, C. B., & Pruessner, J. C. (2013) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2013.12070950 · Cited by 318
Brain development and aging: Overlapping and unique patterns of change
Tamnes, C. K., Walhovd, K. B., Dale, A. M., Østby, Y., Grydeland, H., Richardson, G., Westlye, L. T., Roddey, J. C., Hagler, D. J., Due-Tønnessen, P., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.039 · Cited by 287
Shaw, P., Malek, M., Watson, B., Greenstein, D., de Rossi, P., & Sharp, W. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.04.007 · Cited by 303
Boileau, I., Dagher, A., Leyton, M., Gunn, R. N., Baker, G. B., Diksic, M., & Benkelfat, C. (2006) · Archives of General Psychiatry · doi:10.1001/archpsyc.63.12.1386 · Cited by 305
Pruessner, J. C. (2002) · Cerebral Cortex · doi:10.1093/cercor/12.12.1342 · Cited by 297
Age and gender predict volume decline in the anterior and posterior hippocampus in early adulthood
Pruessner, J. C., Collins, D. L., Pruessner, M., & Evans, A. C. (2001) · The Journal of Neuroscience · doi:10.1523/jneurosci.21-01-00194.2001 · Cited by 292
Self-citationWhat are normal relaxation times of tissues at 3 T?
Bojorquez, J. Z., Bricq, S., Acquitter, C., Brunotte, F., Walker, P. M., & Lalande, A. (2017) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2016.08.021 · Cited by 286
Ghosh, S. S., Kakunoori, S., Augustinack, J., Nieto-Castanon, A., Kovelman, I., Gaab, N., Christodoulou, J. A., Triantafyllou, C., Gabrieli, J. D. E., & Fischl, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.075 · Cited by 281
A large, open source dataset of stroke anatomical brain images and manual lesion segmentations
Liew, S.-L., Anglin, J. M., Banks, N. W., Sondag, M., Ito, K. L., Kim, H., Chan, J., Ito, J., Jung, C., Khoshab, N., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.11 · Cited by 283
Tisserand, D. J. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh057 · Cited by 271
Psychostimulant treatment and the developing cortex in attention deficit hyperactivity disorder
Shaw, P., Sharp, W. S., Morrison, M., Eckstrand, K., Greenstein, D. K., Clasen, L. S., Evans, A. C., & Rapoport, J. L. (2009) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2008.08050781 · Cited by 261
Self-citationMRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans
Mendrik, A. M., Vincken, K. L., Kuijf, H. J., Breeuwer, M., Bouvy, W. H., de Bresser, J., Alansary, A., de Bruijne, M., Carass, A., El-Baz, A., et al. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/813696 · Cited by 261
Josephs, K. A., Dickson, D. W., Tosakulwong, N., Weigand, S. D., Murray, M. E., Petrucelli, L., Liesinger, A. M., Senjem, M. L., Spychalla, A. J., Knopman, D. S., et al. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30284-3 · Cited by 252
Li, G., Wang, L., Shi, F., Lyall, A. E., Lin, W., Gilmore, J. H., & Shen, D. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.3976-13.2014 · Cited by 251
Normative brain size variation and brain shape diversity in humans
Reardon, P. K., Seidlitz, J., Vandekar, S., Liu, S., Patel, R., Park, M. T. M., Alexander-Bloch, A., Clasen, L. S., Blumenthal, J. D., Lalonde, F. M., et al. (2018) · Science · doi:10.1126/science.aar2578 · Cited by 255
Urošević, S., Collins, P., Muetzel, R., Lim, K., & Luciana, M. (2012) · Developmental Psychology · doi:10.1037/a0027502 · Cited by 258
Harvey, P.-O., Pruessner, J., Czechowska, Y., & Lepage, M. (2007) · Molecular Psychiatry · doi:10.1038/sj.mp.4002021 · Cited by 259
Measurement of cortical thickness using an automated 3-D algorithm: a validation study
Kabani, N., Le Goualher, G., MacDonald, D., & Evans, A. C. (2001) · NeuroImage · doi:10.1006/nimg.2000.0652 · Cited by 250
Self-citationMapping corpus callosum deficits in autism: An index of aberrant cortical connectivity
Vidal, C. N., Nicolson, R., DeVito, T. J., Hayashi, K. M., Geaga, J. A., Drost, D. J., Williamson, P. C., Rajakumar, N., Sui, Y., Dutton, R. A., et al. (2006) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.11.011 · Cited by 244
Shaw, P., Gornick, M., Lerch, J., Addington, A., Seal, J., Greenstein, D., Sharp, W., Evans, A., Giedd, J. N., Castellanos, F. X., et al. (2007) · Archives of General Psychiatry · doi:10.1001/archpsyc.64.8.921 · Cited by 242
Self-citationDevelopmental Changes in Organization of Structural Brain Networks
Khundrakpam, B. S., Reid, A., Brauer, J., Carbonell, F., Lewis, J., Ameis, S., Karama, S., Lee, J., Chen, Z., Das, S., et al. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs187 · Cited by 234
Self-citationSex and age differences in atrophic rates: an ADNI study with n=1368 MRI scans
Hua, X., Hibar, D. P., Lee, S., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.033 · Cited by 234
Mapping Region-Specific Longitudinal Cortical Surface Expansion from Birth to 2 Years of Age
Li, G., Nie, J., Wang, L., Shi, F., Lin, W., Gilmore, J. H., & Shen, D. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs265 · Cited by 220
Structural MRI biomarkers for preclinical and mild Alzheimer's disease
Fennema‐Notestine, C., Hagler, D. J., McEvoy, L. K., Fleisher, A. S., Wu, E. H., Karow, D. S., & Dale, A. M. (2009) · Human Brain Mapping · doi:10.1002/hbm.20744 · Cited by 233
Zhu, X., Suk, H.-I., Wang, L., Lee, S.-W., & Shen, D. (2017) · Medical Image Analysis · doi:10.1016/j.media.2015.10.008 · Cited by 224
Yucel, M., Stuart, G. W., Maruff, P., Velakoulis, D., Crowe, S. F., Savage, G., & Pantelis, C. (2001) · Cerebral Cortex · doi:10.1093/cercor/11.1.17 · Cited by 222
Sandman, C. A., Buss, C., Head, K., & Davis, E. P. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.06.025 · Cited by 219
Alexander-Bloch, A., Clasen, L., Stockman, M., Ronan, L., Lalonde, F., Giedd, J., & Raznahan, A. (2016) · Human Brain Mapping · doi:10.1002/hbm.23180 · Cited by 178
Sowell, E. R. (2002) · Cerebral Cortex · doi:10.1093/cercor/12.1.17 · Cited by 212
The contribution of geometry to the human connectome
Roberts, J. A., Perry, A., Lord, A. R., Roberts, G., Mitchell, P. B., Smith, R. E., Calamante, F., & Breakspear, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.009 · Cited by 213
Proal, E. (2011) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.117 · Cited by 207
A survey of methods for 3D histology reconstruction
Pichat, J., Iglesias, J. E., Yousry, T., Ourselin, S., & Modat, M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.004 · Cited by 207
Zhu, X., Suk, H.-I., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.078 · Cited by 202
Nordahl, C. W. (2012) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.145 · Cited by 197
Metadata
- Authors
- Sled, J. G., Zijdenbos, A. P., & Evans, A. C.
- Affiliations
- Montreal Neurological Institute and Hospital (CA)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 1998
- Volume
- 17
- Issue
- 1
- Pages
- 87-97
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2157848968
- Cited by
- 4,731
- References
- 51
- Citations by year
- 2026: 92 · 2025: 148 · 2024: 159 · 2023: 176 · 2022: 187 · 2021: 240 · 2020: 227 · 2019: 257 · 2018: 246 · 2017: 260 · 2016: 251 · 2015: 303 · 2014: 293 · 2013: 298 · 2012: 275
- FWCI
- 36.12
- Type
- journal article
- Language
- en
- Open access
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Library and Archives Canada (Government of Canada) (submittedVersion)
- eScholarship@McGill (McGill) (submittedVersion)
- McGill-DEV
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Keywords
- magnetic resonance imaging, bias field estimation, iterative estimation, N3
- MeSH terms
- Brain, Humans, Magnetic Resonance Imaging, Models, Theoretical
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
