The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
N4ITK: Improved N3 Bias Correction
Open accessAbstract
A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized (3)He lung image data, and 9.4T postmortem hippocampus 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.
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 nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731
Research works printed but not linked: 28
- 10.1007/11812715_10 · DOI
- ibanez, 2005, The ITK Software Guide
- tustison, 2009, N4ITK: Nick's N3 ITK implementation for MRI bias field correction, Insight J
- riesenfeld, 1975, Applications of B-Spline Approximation to Geometric Problems of Computer-Aided Design
- 10.1093/imamat/10.2.134 · DOI · Paper page
- 10.1016/0021-9045(72)90080-9 · DOI · Paper page
- 10.1016/0010-4485(94)00018-9 · DOI · Paper page
- 10.1109/34.88568 · DOI · Paper page
- 10.1073/pnas.52.4.947 · DOI · Paper page
- 10.1109/TMI.2005.861015 · doi:10.1109/tmi.2005.861015
- 10.1002/jmri.20104 · doi:10.1002/jmri.20104
- 10.1016/j.ejrad.2004.08.002 · DOI
- 10.1109/TMI.2006.891486 · DOI · Paper page
- 10.1016/j.neuroimage.2007.10.026 · DOI
- 10.1007/s10334-003-0028-2 · DOI
- 10.1006/nimg.2001.0756 · DOI
- 10.1109/2945.620490 · DOI · Paper page
- 10.1016/j.neuroimage.2009.06.039 · DOI
- 10.1155/IJBI/2006/49515 · DOI
- tustison, 2005, <formula formulatype="inline"> <tex notation="tex">${n}$</tex></formula>-d <formula formulatype="inline"> <tex notation="tex">${c}^{k}$</tex></formula> b-spline scattered data approximation, Insight J, 1
- 10.1016/j.media.2005.09.004 · DOI
- hjelle, 0, Approximation of scattered data with multilevel B-splines
- 10.1016/j.media.2007.03.001 · DOI
- 10.1117/12.467069 · DOI
- 10.1109/42.712135 · DOI · Paper page
- 10.1016/j.neuroimage.2008.08.042 · DOI
- 10.1109/TMI.2006.883453 · DOI · Paper page
- 10.1016/j.neuroimage.2006.03.052 · DOI · Paper page
Cited by
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Self-citationfMRIPrep: A robust preprocessing pipeline for functional MRI
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., et al. (2019) · Nature Methods · doi:10.1038/s41592-018-0235-4 · Cited by 3,905
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
MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation
Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116137 · Cited by 2,974
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735
Harmonization of cortical thickness measurements across scanners and sites
Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., Adams, P., Cooper, C., Fava, M., McGrath, P. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.024 · Cited by 1,580
Self-citationCiric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., Shinohara, R. T., Elliott, M. A., Eickhoff, S. B., Davatzikos, C., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.020 · Cited by 1,042
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
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., & Fan, Y. (2018) · Medical Image Analysis · doi:10.1016/j.media.2017.10.002 · Cited by 813
Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging
Horn, A., Li, N., Dembek, T. A., Kappel, A., Boulay, C., Ewert, S., Tietze, A., Husch, A., Perera, T., Neumann, W.-J., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.068 · Cited by 739
Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements
Tustison, N. J., Cook, P. A., Klein, A., Song, G., Das, S. R., Duda, J. T., Kandel, B. M., van Strien, N., Stone, J. R., Gee, J. C., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.044 · Cited by 670
Self-citationKhan, U. A., Liu, L., Provenzano, F. A., Berman, D. E., Profaci, C. P., Sloan, R., Mayeux, R., Duff, K. E., & Small, S. A. (2014) · Nature Neuroscience · doi:10.1038/nn.3606 · Cited by 561
Linked dimensions of psychopathology and connectivity in functional brain networks
Xia, C. H., Ma, Z., Ciric, R., Gu, S., Betzel, R. F., Kaczkurkin, A. N., Calkins, M. E., Cook, P. A., García de la Garza, A., Vandekar, S. N., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05317-y · Cited by 437
Self-citationFederated 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
Bron, E. E., Smits, M., van der Flier, W. M., Vrenken, H., Barkhof, F., Scheltens, P., Papma, J. M., Steketee, R. M. E., Méndez Orellana, C., Meijboom, R., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.048 · Cited by 352
Analysis of task-based functional MRI data preprocessed with fMRIPrep
Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., Kent, J. D., Goncalves, M., DuPre, E., Gomez, D. E. P., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0327-3 · Cited by 315
Fibre-specific white matter reductions in Alzheimer’s disease and mild cognitive impairment
Mito, R., Raffelt, D., Dhollander, T., Vaughan, D. N., Tournier, J.-D., Salvado, O., Brodtmann, A., Rowe, C. C., Villemagne, V. L., & Connelly, A. (2018) · Brain · doi:10.1093/brain/awx355 · Cited by 309
Gennatas, E. D., Avants, B. B., Wolf, D. H., Satterthwaite, T. D., Ruparel, K., Ciric, R., Hakonarson, H., Gur, R. E., & Gur, R. C. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3550-16.2017 · Cited by 302
Self-citationTruhn, D., Schrading, S., Haarburger, C., Schneider, H., Merhof, D., & Kuhl, C. (2019) · Radiology · doi:10.1148/radiol.2018181352 · Cited by 273
Hussein, S., Kandel, P., Bolan, C. W., Wallace, M. B., & Bagci, U. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2894349 · Cited by 285
The effects of SIFT on the reproducibility and biological accuracy of the structural connectome
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.004 · Cited by 266
Impaired Glymphatic Transport in Spontaneously Hypertensive Rats
Mortensen, K. N., Sanggaard, S., Mestre, H., Lee, H., Kostrikov, S., Xavier, A. L. R., Gjedde, A., Benveniste, H., & Nedergaard, M. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.1974-18.2019 · Cited by 266
Converging evidence for the neuroanatomic basis of combinatorial semantics in the angular gyrus
Price, A. R., Bonner, M. F., Peelle, J. E., & Grossman, M. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.3446-14.2015 · Cited by 262
COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography
Daducci, A., Dal Palu, A., Lemkaddem, A., & Thiran, J.-P. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2352414 · Cited by 255
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Thaha, M. M., Kumar, K. P. M., Murugan, B. S., Dhanasekeran, S., Vijayakarthick, P., & Selvi, A. S. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1416-0 · Cited by 244
Hiasa, Y., Otake, Y., Takao, M., Matsuoka, T., Takashima, K., Carass, A., Prince, J. L., Sugano, N., & Sato, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00536-8_4 · Cited by 221
Deep learning for segmentation of brain tumors: Impact of cross‐institutional training and testing
AlBadawy, E. A., Saha, A., & Mazurowski, M. A. (2018) · Medical Physics · doi:10.1002/mp.12752 · Cited by 208
AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation
Coupé, P., Mansencal, B., Clément, M., Giraud, R., Denis de Senneville, B., Ta, V.-T., Lepetit, V., & Manjon, J. V. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117026 · Cited by 206
Unsupervised MR-to-CT Synthesis Using Structure-Constrained CycleGAN
Yang, H., Sun, J., Carass, A., Zhao, C., Lee, J., Prince, J. L., & Xu, Z. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3015379 · Cited by 204
Differential diagnosis of neurodegenerative diseases using structural MRI data
Koikkalainen, J., Rhodius-Meester, H., Tolonen, A., Barkhof, F., Tijms, B., Lemstra, A. W., Tong, T., Guerrero, R., Schuh, A., Ledig, C., et al. (2016) · NeuroImage: Clinical · doi:10.1016/j.nicl.2016.02.019 · Cited by 193
Hu, K., Gan, Q., Zhang, Y., Deng, S., Xiao, F., Huang, W., Cao, C., & Gao, X. (2019) · IEEE Access · doi:10.1109/access.2019.2927433 · Cited by 188
Habes, M., Pomponio, R., Shou, H., Doshi, J., Mamourian, E., Erus, G., Nasrallah, I., Launer, L. J., Rashid, T., Bilgel, M., et al. (2021) · Alzheimer's & Dementia · doi:10.1002/alz.12178 · Cited by 173
Moradmand, H., Aghamiri, S. M. R., & Ghaderi, R. (2020) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.12795 · Cited by 173
Eitel, F., Soehler, E., Bellmann-Strobl, J., Brandt, A. U., Ruprecht, K., Giess, R. M., Kuchling, J., Asseyer, S., Weygandt, M., Haynes, J.-D., et al. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.102003 · Cited by 171
Complex Trajectories of Brain Development in the Healthy Human Fetus
Andescavage, N. N., du Plessis, A., McCarter, R., Serag, A., Evangelou, I., Vezina, G., Robertson, R., & Limperopoulos, C. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw306 · Cited by 163
Bhagwat, N., Viviano, J. D., Voineskos, A. N., Chakravarty, M. M., & Initiative, A. D. N. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006376 · Cited by 157
Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma
Beig, N., Patel, J., Prasanna, P., Hill, V., Gupta, A., Correa, R., Bera, K., Singh, S., Partovi, S., Varadan, V., et al. (2018) · Scientific Reports · doi:10.1038/s41598-017-18310-0 · Cited by 157
Automatic quantification of normal cortical folding patterns from fetal brain MRI
Wright, R., Kyriakopoulou, V., Ledig, C., Rutherford, M. A., Hajnal, J. V., Rueckert, D., & Aljabar, P. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.01.034 · Cited by 134
Microstructure informed tractography: pitfalls and open challenges
Daducci, A., Dal Palú, A., Descoteaux, M., & Thiran, J.-P. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00247 · Cited by 138
Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRI
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-30858-6_12 · Cited by 135
M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer
Jang, J., & Hwang, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02006 · Cited by 133
Linardos, A., Kushibar, K., Walsh, S., Gkontra, P., & Lekadir, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-07186-4 · Cited by 130
Khan, S., Vasung, L., Marami, B., Rollins, C. K., Afacan, O., Ortinau, C. M., Yang, E., Warfield, S. K., & Gholipour, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.030 · Cited by 115
Shou, H., Yang, Z., Satterthwaite, T. D., Cook, P. A., Bruce, S. E., Shinohara, R. T., Rosenberg, B., & Sheline, Y. I. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.01.030 · Cited by 108
Self-citationBrain Tumour Segmentation based on Extremely Randomized Forest with high-level features
Pinto, A., Pereira, S., Correia, H., Oliveira, J., Rasteiro, D. M. L. D., & Silva, C. A. (2015) · 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319032 · Cited by 108
Batalle, D., O'Muircheartaigh, J., Makropoulos, A., Kelly, C. J., Dimitrova, R., Hughes, E. J., Hajnal, J. V., Zhang, H., Alexander, D. C., Edwards, A. D., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.046 · Cited by 89
Jamadar, S. D., Ward, P. G., Liang, E. X., Orchard, E. R., Chen, Z., & Egan, G. F. (2020) · doi:10.1101/2020.05.01.071662 · Cited by 98
Bron, E. E., Steketee, R. M. E., Houston, G. C., Oliver, R. A., Achterberg, H. C., Loog, M., van Swieten, J. C., Hammers, A., Niessen, W. J., Smits, M., et al. (2014) · Human Brain Mapping · doi:10.1002/hbm.22522 · Cited by 93
Civier, O., Smith, R. E., Yeh, C.-H., Connelly, A., & Calamante, F. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.039 · Cited by 95
Liang, Y., Schott, D., Zhang, Y., Wang, Z., Nasief, H., Paulson, E., Hall, W., Knechtges, P., Erickson, B., & Li, X. A. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2020.01.021 · Cited by 90
3-D volumetric MRI evaluation of the placenta in fetuses with complex congenital heart disease
Andescavage, N., Yarish, A., Donofrio, M., Bulas, D., Evangelou, I., Vezina, G., McCarter, R., duPlessis, A., & Limperopoulos, C. (2015) · Placenta · doi:10.1016/j.placenta.2015.06.013 · Cited by 89
Functional properties of resting state networks in healthy full-term newborns
De Asis-Cruz, J., Bouyssi-Kobar, M., Evangelou, I., Vezina, G., & Limperopoulos, C. (2015) · Scientific Reports · doi:10.1038/srep17755 · Cited by 85
Reliable individual differences in fine-grained cortical functional architecture
Feilong, M., Nastase, S. A., Guntupalli, J. S., & Haxby, J. V. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.029 · Cited by 68
Association of Prenatal Maternal Anxiety With Fetal Regional Brain Connectivity
De Asis-Cruz, J., Krishnamurthy, D., Zhao, L., Kapse, K., Vezina, G., Andescavage, N., Quistorff, J., Lopez, C., & Limperopoulos, C. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.22349 · Cited by 81
Stone, J. R., Avants, B. B., Tustison, N. J., Wassermann, E. M., Gill, J., Polejaeva, E., Dell, K. C., Carr, W., Yarnell, A. M., LoPresti, M. L., et al. (2020) · Journal of Neurotrauma · doi:10.1089/neu.2020.7141 · Cited by 81
Self-citationLarsen, B., Cui, Z., Adebimpe, A., Pines, A., Alexander-Bloch, A., Bertolero, M., Calkins, M. E., Gur, R. E., Gur, R. C., Mahadevan, A. S., et al. (2022) · Science Advances · doi:10.1126/sciadv.abj8750 · Cited by 80
Diminished Cortical Thickness Is Associated with Impulsive Choice in Adolescence
Pehlivanova, M., Wolf, D. H., Sotiras, A., Kaczkurkin, A. N., Moore, T. M., Ciric, R., Cook, P. A., Garcia de La Garza, A., Rosen, A. F. G., Ruparel, K., et al. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.2200-17.2018 · Cited by 79
Self-citationNeocortical origin and progression of gray matter atrophy in nonamnestic Alzheimer's disease
Phillips, J. S., Da Re, F., Dratch, L., Xie, S. X., Irwin, D. J., McMillan, C. T., Vaishnavi, S. N., Ferrarese, C., Lee, E. B., Shaw, L. M., et al. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.11.008 · Cited by 74
Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects
Glocker, B., Robinson, R., Castro, D. C., Dou, Q., & Konukoglu, E. (2019) · arXiv · doi:10.48550/arxiv.1910.04597 · Cited by 74
Medaglia, J. D., Satterthwaite, T. D., Kelkar, A., Ciric, R., Moore, T. M., Ruparel, K., Gur, R. C., Gur, R. E., & Bassett, D. S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.048 · Cited by 64
Assessing atrophy measurement techniques in dementia: results from the MIRIAD atrophy challenge
Cash, D. M., Frost, C., Iheme, L. O., Ünay, D., Kandemir, M., Fripp, J., Salvado, O., Bourgeat, P., Reuter, M., Fischl, B., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.087 · Cited by 72
Self-citationEpelbaum, S., Burgos, N., Canney, M., Matthews, D., Houot, M., Santin, M. D., Desseaux, C., Bouchoux, G., Stroer, S., Martin, C., et al. (2022) · Alzheimer's Research & Therapy · doi:10.1186/s13195-022-00981-1 · Cited by 72
Bassoon contributes to tau-seed propagation and neurotoxicity
Martinez, P., Patel, H., You, Y., Jury, N., Perkins, A., Lee-Gosselin, A., Taylor, X., You, Y., Viana Di Prisco, G., Huang, X., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01191-6 · Cited by 68
Tau PET imaging predicts cognition in atypical variants of Alzheimer's disease
Phillips, J. S., Das, S. R., McMillan, C. T., Irwin, D. J., Roll, E. E., Da Re, F., Nasrallah, I. M., Wolk, D. A., & Grossman, M. (2018) · Human Brain Mapping · doi:10.1002/hbm.23874 · Cited by 68
Humphreys, K. L., Camacho, M. C., Roth, M. C., & Estes, E. C. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100877 · Cited by 67
A review of Methods for Bias Correction in Medical Images
Song, S., Zheng, Y., & He, Y. (2017) · Biomedical Engineering Review · doi:10.18103/bme.v3i1.1550 · Cited by 62
Self-citationFunctional brain connectivity in ex utero premature infants compared to in utero fetuses
De Asis-Cruz, J., Kapse, K., Basu, S. K., Said, M., Scheinost, D., Murnick, J., Chang, T., du Plessis, A., & Limperopoulos, C. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117043 · Cited by 63
Yazdan-Panah, A., Schmidt-Mengin, M., Ricigliano, V. A. G., Soulier, T., Stankoff, B., & Colliot, O. (2023) · NeuroImage: Clinical · doi:10.1016/j.nicl.2023.103368 · Cited by 58
Ts66Yah, a mouse model of down syndrome with improved construct and face validity
Duchon, A., del Mar Muñiz Moreno, M., Chevalier, C., Nalesso, V., Andre, P., Fructuoso-Castellar, M., Mondino, M., Po, C., Noblet, V., Birling, M.-C., et al. (2022) · Disease Models & Mechanisms · doi:10.1242/dmm.049721 · Cited by 58
Rathore, S., Bakas, S., Pati, S., Akbari, H., Kalarot, R., Sridharan, P., Rozycki, M., Bergman, M., Tunc, B., Verma, R., et al. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-75238-9_12 · Cited by 57
Barnett, A. J., Nguyen, M., Spargo, J., Yadav, R., Cohn-Sheehy, B. I., & Ranganath, C. (2024) · Neuron · doi:10.1016/j.neuron.2023.10.010 · Cited by 55
Neural processing of the reward value of pleasant odorants
Midroit, M., Chalençon, L., Renier, N., Milton, A., Thevenet, M., Sacquet, J., Breton, M., Forest, J., Noury, N., Richard, M., et al. (2021) · Current Biology · doi:10.1016/j.cub.2021.01.066 · Cited by 55
Holmes, H. E., Powell, N. M., Ma, D., Ismail, O., Harrison, I. F., Wells, J. A., Colgan, N., O'Callaghan, J. M., Johnson, R. A., Murray, T. K., et al. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00020 · Cited by 55
Delettre, C., Messé, A., Dell, L.-A., Foubet, O., Heuer, K., Larrat, B., Meriaux, S., Mangin, J.-F., Reillo, I., de Juan Romero, C., et al. (2019) · Network Neuroscience · doi:10.1162/netn_a_00098 · Cited by 55
Tong, T., Ledig, C., Guerrero, R., Schuh, A., Koikkalainen, J., Tolonen, A., Rhodius, H., Barkhof, F., Tijms, B., Lemstra, A. W., et al. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.06.012 · Cited by 54
Identifying schizophrenia subgroups using clustering and supervised learning
Talpalaru, A., Bhagwat, N., Devenyi, G. A., Lepage, M., & Chakravarty, M. M. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2019.05.044 · Cited by 53
Effects of MRI scanner manufacturers in classification tasks with deep learning models
Kushol, R., Parnianpour, P., Wilman, A. H., Kalra, S., & Yang, Y.-H. (2023) · Scientific Reports · doi:10.1038/s41598-023-43715-5 · Cited by 54
A collaborative resource platform for non-human primate neuroimaging
Messinger, A., Sirmpilatze, N., Heuer, K., Loh, K. K., Mars, R. B., Sein, J., Xu, T., Glen, D., Jung, B., Seidlitz, J., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117519 · Cited by 52
Onset pattern of nigrostriatal denervation in early Parkinson’s disease
Pineda-Pardo, J. A., Sánchez-Ferro, Á., Monje, M. H. G., Pavese, N., & Obeso, J. A. (2022) · Brain · doi:10.1093/brain/awab378 · Cited by 51
Pati, S., Baid, U., Edwards, B., Sheller, M. J., Foley, P., Anthony Reina, G., Thakur, S., Sako, C., Bilello, M., Davatzikos, C., et al. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac9449 · Cited by 51
Imaging the accumulation and suppression of tau pathology using multiparametric MRI
Holmes, H. E., Colgan, N., Ismail, O., Ma, D., Powell, N. M., O'Callaghan, J. M., Harrison, I. F., Johnson, R. A., Murray, T. K., Ahmed, Z., et al. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.12.001 · Cited by 51
Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification
Hett, K., Ta, V.-T., Catheline, G., Tourdias, T., Manjón, J. V., Coupé, P., Initiative, A. D. N., Weiner, M. W., Aisen, P., Petersen, R., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-49970-9 · Cited by 49
Kamiya, K., Hori, M., Irie, R., Miyajima, M., Nakajima, M., Kamagata, K., Tsuruta, K., Saito, A., Nakazawa, M., Suzuki, Y., et al. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.03.003 · Cited by 49
ABCD1 dysfunction alters white matter microvascular perfusion
Lauer, A., Da, X., Hansen, M. B., Boulouis, G., Ou, Y., Cai, X., Liberato Celso Pedrotti, A., Kalpathy-Cramer, J., Caruso, P., Hayden, D. L., et al. (2017) · Brain · doi:10.1093/brain/awx262 · Cited by 48
Bouts, M. J. R. J., Möller, C., Hafkemeijer, A., van Swieten, J. C., Dopper, E., van der Flier, W. M., Vrenken, H., Wink, A. M., Pijnenburg, Y. A. L., Scheltens, P., et al. (2018) · Journal of Alzheimer’s Disease · doi:10.3233/jad-170893 · Cited by 47
Wright, D. K., O'Brien, T. J., Mychasiuk, R., & Shultz, S. R. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.01.033 · Cited by 46
Billot, A., Lai, S., Varkanitsa, M., Braun, E. J., Rapp, B., Parrish, T. B., Higgins, J., Kurani, A. S., Caplan, D., Thompson, C. K., et al. (2022) · Stroke · doi:10.1161/strokeaha.121.036749 · Cited by 47
Srinivasan, D., Erus, G., Doshi, J., Wolk, D. A., Shou, H., Habes, M., & Davatzikos, C. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117248 · Cited by 45
“Image preprocessing in MUSE included inhomo-geneity correction with N4 ( Tustison et al., 2010 ) and multi-atlas skull-stripping ( Doshi et al., 2016 ).”
MethodLeveraging shared connectivity to aggregate heterogeneous datasets into a common response space
Nastase, S. A., Liu, Y.-F., Hillman, H., Norman, K. A., & Hasson, U. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116865 · Cited by 42
Colon-Perez, L. M., Ibanez, K. R., Suarez, M., Torroella, K., Acuna, K., Ofori, E., Levites, Y., Vaillancourt, D. E., Golde, T. E., Chakrabarty, P., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116138 · Cited by 41
Global Network Organization of the Fetal Functional Connectome
De Asis-Cruz, J., Andersen, N., Kapse, K., Khrisnamurthy, D., Quistorff, J., Lopez, C., Vezina, G., & Limperopoulos, C. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhaa410 · Cited by 40
Unno, K., Sumiyoshi, A., Konishi, T., Hayashi, M., Taguchi, K., Muguruma, Y., Inoue, K., Iguchi, K., Nonaka, H., Kawashima, R., et al. (2020) · Nutrients · doi:10.3390/nu12010174 · Cited by 39
Badea, A., Kane, L., Anderson, R. J., Qi, Y., Foster, M., Cofer, G. P., Medvitz, N., Buckley, A. F., Badea, A. K., Wetsel, W. C., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.014 · Cited by 39
The human brain uses spatial schemas to represent segmented environments
Peer, M., & Epstein, R. A. (2021) · Current Biology · doi:10.1016/j.cub.2021.08.012 · Cited by 38
High-amplitude co-fluctuations in cortical activity drive functional connectivity
Esfahlani, F. Z., Jo, Y., Faskowitz, J., Byrge, L., Kennedy, D. P., Sporns, O., & Betzel, R. F. (2019) · doi:10.1101/800045 · Cited by 38
Batail, J.-M., Xiao, X., Azeez, A., Tischler, C., Kratter, I. H., Bishop, J. H., Saggar, M., & Williams, N. R. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02537-9 · Cited by 33
The association between white matter hyperintensities and amyloid and tau deposition
Alban, S. L., Lynch, K. M., Ringman, J. M., Toga, A. W., Chui, H. C., Sepehrband, F., & Choupan, J. (2023) · NeuroImage: Clinical · doi:10.1016/j.nicl.2023.103383 · Cited by 35
Ma, D., Holmes, H. E., Cardoso, M. J., Modat, M., Harrison, I. F., Powell, N. M., O’Callaghan, J. M., Ismail, O., Johnson, R. A., O’Neill, M. J., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00011 · Cited by 36
Lee, W.-K., Wu, C.-C., Lee, C.-C., Lu, C.-F., Yang, H.-C., Huang, T.-H., Lin, C.-Y., Chung, W.-Y., Wang, P.-S., Wu, H.-M., et al. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101911 · Cited by 35
Metadata
- Authors
- Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2010-06
- Volume
- 29
- Issue
- 6
- Pages
- 1310-1320
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2117340355
- Cited by
- 6,073
- References
- 1
- FWCI
- 26.17
- Type
- journal article
- Language
- en
- Open access
- green
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
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.
