Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST

Mulder, Emma R.; de Jong, Remko A.; Knol, Dirk L.; van Schijndel, Ronald A.; Cover, Keith S.; Visser, Pieter J.; Barkhof, Frederik; Vrenken, Hugo

doi:10.1016/j.neuroimage.2014.01.058

Cited by 122Dementia and Cognitive Impairment ResearchMedical Image Segmentation TechniquesRetinal Imaging and Analysis

Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST

Mulder, Emma R., de Jong, Remko A., Knol, Dirk L., van Schijndel, Ronald A., Cover, Keith S., Visser, Pieter J., Barkhof, Frederik, Vrenken, Hugo·2014·NeuroImage

DOI 10.1016/j.neuroimage.2014.01.058

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Abstract

Background To measure hippocampal volume change in Alzheimer's disease (AD) or mild cognitive impairment (MCI), expert manual delineation is often used because of its supposed accuracy. It has been suggested that expert outlining yields poorer reproducibility as compared to automated methods, but this has not been investigated. Aim To determine the reproducibilities of expert manual outlining and two common automated methods for measuring hippocampal atrophy rates in healthy aging, MCI and AD. Methods From the Alzheimer's Disease Neuroimaging Initiative (ADNI), 80 subjects were selected: 20 patients with AD, 40 patients with mild cognitive impairment (MCI) and 20 healthy controls (HCs). Left and right hippocampal volume change between baseline and month-12 visit was assessed by using expert manual delineation, and by the automated software packages FreeSurfer (longitudinal processing stream) and FIRST. To assess reproducibility of the measured hippocampal volume change, both back-to-back (BTB) MPRAGE scans available for each visit were analyzed. Hippocampal volume change was expressed in μL, and as a percentage of baseline volume. Reproducibility of the 1-year hippocampal volume change was estimated from the BTB measurements by using linear mixed model to calculate the limits of agreement (LoA) of each method, reflecting its measurement uncertainty. Using the delta method, approximate p-values were calculated for the pairwise comparisons between methods. Statistical analyses…

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Various evaluations have revealed limitations of Freesurfer that might be critical in aging and multi-site studies.
“However, various evaluations have also revealed limitations of that approach (Mccarthy 2015; Keller et al., 2012; Mulder et al., 2014), which might be critical in aging and multi-site studies.”
From A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies (2020) · cites this paper as Mulder et al., 2014

STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965 · see citing works
Akufo and Ibarapa
Beckett, A. H., Rowland, M., & TURNER, P. (1965) · The Lancet · doi:10.1016/s0140-6736(65)91037-8 · Cited by 16,917 · see citing works
Measuring agreement in method comparison studies
Bland, J. M., & Altman, D. G. (1999) · Statistical Methods in Medical Research · doi:10.1177/096228029900800204 · Cited by 9,619 · see citing works
Whole Brain Segmentation
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610 · see citing works
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
Zhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628 · see citing works
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347 · see citing works
Automatically Parcellating the Human Cerebral Cortex
Fischl, B. R. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg087 · Cited by 4,208 · see citing works
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 · see citing works
A Bayesian model of shape and appearance for subcortical brain segmentation
Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.046 · Cited by 2,287 · see citing works
Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072 · see citing works
Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects
Shaw, L. M., Vanderstichele, H., Knapik‐Czajka, M., Clark, C. M., Aisen, P. S., Petersen, R. C., Blennow, K., Soares, H., Simon, A., Lewczuk, P., et al. (2009) · Annals of Neurology · doi:10.1002/ana.21610 · Cited by 2,065 · see citing works
Agreement Between Methods of Measurement with Multiple Observations Per Individual
Bland, J. M., & Altman, D. G. (2007) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543400701329422 · Cited by 1,864 · cited 1× · see citing works
“…patient having two hippocampi, modeling of both ﬁ xed and random effects variables, and handling of missing data, by which it provided superior estimates of each method's limits of agreement than those that could have been obtained from simpler analysis types (Bland and Altman, 1986, 1999, 2007).”
The Alzheimer's Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2005.09.008 · Cited by 1,525 · see citing works
Highly accurate inverse consistent registration: A robust approach
Reuter, M., Rosas, H. D., & Fischl, B. R. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.020 · Cited by 1,378 · see citing works
The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
Weiner, M. W., Veitch, D. P., Aisen, P. S. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Liu, E., et al. (2013) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2013.05.1769 · Cited by 1,011 · see citing works
Rate of medial temporal lobe atrophy in typical aging and Alzheimer's disease
Jack, C. R., Petersen, R. C., Xu, Y., O'Brien, P. C., Smith, G. E., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1998) · Neurology · doi:10.1212/wnl.51.4.993 · Cited by 666 · see citing works
A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes
Morey, R. A., Petty, C. M., Xu, Y., Pannu Hayes, J., Wagner, H. R., Lewis, D. V., LaBar, K. S., Styner, M., & McCarthy, G. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.033 · Cited by 540 · see citing works
The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
Weiner, M. W., Veitch, D. P., Aisen, P. S. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Liu, E., et al. (2011) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.09.172 · Cited by 477 · see citing works
The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements
Gronenschild, E. H. B. M., Habets, P., Jacobs, H. I. L., Mengelers, R., Rozendaal, N., van Os, J., & Marcelis, M. C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038234 · Cited by 354 · see citing works
Who Invented the Delta Method?
Hoef, J. M. V. (2012) · The American Statistician · doi:10.1080/00031305.2012.687494 · Cited by 267 · see citing works
Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging
Wang, L., Swank, J. S., Glick, I. E., Gado, M. H., Miller, M. I., Morris, J. C., & Csernansky, J. G. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00361-6 · Cited by 263 · see citing works
Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease
Leung, K. K., Barnes, J., Ridgway, G. R., Bartlett, J. W., Clarkson, M. J., Macdonald, K. E., Schuff, N. W., Fox, N. C., & Sebastien Ourselin, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.018 · Cited by 244 · see citing works
Validation of hippocampal volumes measured using a manual method and two automated methods (FreeSurfer and IBASPM) in chronic major depressive disorder
Tae, W., Kim, S. S., Lee, K. U., Nam, E., & Kim, K. W. (2008) · Neuroradiology · doi:10.1007/s00234-008-0383-9 · Cited by 242 · see citing works
Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging
Kikinis, R., Shenton, M. E., Gerig, G., Martin, J., Anderson, M., Metcalf, D., Guttmann, C. R. G., McCarley, R. W., Jolesz, F. A., Lorensen, W. E., et al. (1992) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.1880020603 · Cited by 237 · see citing works
Age-related differences in regional brain volumes: A comparison of optimized voxel-based morphometry to manual volumetry
Kennedy, K. M., Erickson, K. I., Rodrigue, K. M., Voss, M. W., Colcombe, S. J., Kramer, A. F., Acker, J. D., & Raz, N. (2008) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2007.12.020 · Cited by 214 · see citing works
Comparison of automated and manual MRI volumetry of hippocampus in normal aging and dementia
Hsu, Y., Schuff, N. W., Du, A., Mark, K. E., Zhu, X., Hardin, D., & Weiner, M. W. (2002) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.10163 · Cited by 212 · see citing works
Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts
van der Lijn, F., den Heijer, T., Breteler, M. M. B., & Niessen, W. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.07.058 · Cited by 209 · see citing works
A practical approach to Bland-Altman plots and variation coefficients for log transformed variables
Euser, A. M., Dekker, F. W., & le Cessie, S. (2008) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2007.11.003 · Cited by 171 · see citing works
Hippocampal volume assessment in temporal lobe epilepsy: How good is automated segmentation?
Pardoe, H., Pell, G. S., Abbott, D. F., & Jackson, G. D. (2009) · Epilepsia · doi:10.1111/j.1528-1167.2009.02243.x · Cited by 164 · see citing works
Survey of Protocols for the Manual Segmentation of the Hippocampus: Preparatory Steps Towards a Joint EADC-ADNI Harmonized Protocol
Boccardi, M., Ganzola, R., Bocchetta, M., Pievani, M., Redolfi, A., Bartzokis, G., Camicioli, R. M., Csernansky, J. G., de Leon, M. J., deToledo‐Morrell, L., et al. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-0004 · Cited by 140 · see citing works
Disease Tracking Markers for Alzheimer's Disease at the Prodromal (MCI) Stage
Drago, V., Babiloni, C., Bartrés‐Faz, D., Caroli, A., Bosch, B., Hensch, T., Didic, M., Klafki, H., Pievani, M., Jovicich, J., et al. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-0043 · Cited by 138 · see citing works
Automated Hippocampal Segmentation by Regional Fluid Registration of Serial MRI: Validation and Application in Alzheimer's Disease
Crum, W. R., Scahill, R. I., & Fox, N. C. (2001) · NeuroImage · doi:10.1006/nimg.2001.0744 · Cited by 137 · see citing works
AddNeuroMed and ADNI: Similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America
Westman, E., Simmons, A., Muehlboeck, J., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Soininen, H. S., Weiner, M. W., Lovestone, S., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.065 · Cited by 136 · see citing works
Reliability and validity of MRI-based automated volumetry software relative to auto-assisted manual measurement of subcortical structures in HIV-infected patients from a multisite study
Dewey, J. J., Hana, G., Russell, T., Price, J., McCaffrey, D., Harezlak, J., Sem, E., Anyanwu, J. C., Guttmann, C. R. G., NAVIA, B. A., et al. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.033 · Cited by 126 · see citing works
A comparison of methods for the automated calculation of volumes and atrophy rates in the hippocampus
Barnes, J., Foster, J., Boyes, R. G., Pepple, T., Moore, E. K., Schott, J. M., Frost, C. D., Scahill, R. I., & Fox, N. C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.01.012 · Cited by 111 · see citing works
Manual validation of FreeSurfer's automated hippocampal segmentation in normal aging, mild cognitive impairment, and Alzheimer Disease subjects
Sánchez‐Benavides, G., Gómez‐Ansón, B., Sainz, A., Vives‐Gilabert, Y., Delfino, M., & Peña‐Casanova, J. (2010) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2009.10.011 · Cited by 109 · see citing works
Atrophy patterns in Alzheimer's disease and semantic dementia: A comparison of FreeSurfer and manual volumetric measurements
Lehmann, M., Douiri, A., Kim, L. G., Marc Modat, A., Chan, D., Sebastien Ourselin, A., Barnes, J., & Fox, N. C. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.056 · Cited by 106 · see citing works
Measuring temporal morphological changes robustly in brain MR images via 4-dimensional template warping
Shen, D. G., & Davatzikos, C. A. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.12.015 · Cited by 106 · see citing works
Differentiating AD from aging using semiautomated measurement of hippocampal atrophy rates
Barnes, J., Scahill, R. I., Boyes, R. G., Frost, C. D., Lewis, E. B., Rossor, C. L., Rossor, M. N., & Fox, N. C. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.06.028 · Cited by 103 · see citing works
Reduced sample sizes for atrophy outcomes in Alzheimer's disease trials: baseline adjustment
Schott, J., Bartlett, J., Barnes, J., Leung, K. K., Ourselin, S., & Fox, N. C. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.011 · Cited by 84 · see citing works
Comparison of Manual and Automated Determination of Hippocampal Volumes in MCI and Early AD
Shen, L., Saykin, A. J., Kim, S., Firpi, H. A., West, J. D., Risacher, S. L., McDonald, B. C., McHugh, T., Wishart, H. A., & Flashman, L. A. (2010) · Brain Imaging and Behavior · doi:10.1007/s11682-010-9088-x · Cited by 80 · see citing works
Power Calculations for Clinical Trials in Alzheimer's Disease
Ard, M. C., & Edland, S. D. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-0062 · Cited by 74 · see citing works
Gradient distortions in MRI: Characterizing and correcting for their effects on SIENA-generated measures of brain volume change
Caramanos, Z., Fonov, V., Francis, S. J., Narayanan, S., Pike, G. B., Collins, D. L., & Arnold, D. L. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.008 · Cited by 74 · see citing works
Evaluation of hippocampal volume based on MR imaging in patients with bipolar affective disorder applying manual and automatic segmentation techniques
Doring, T. M., Kubo, T. T. A., da Cruz, L. C. H., Juruena, M. F., Fainberg, J., Domingues, R. C., & Gasparetto, E. L. (2011) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.22473 · Cited by 69 · see citing works
Appearance-Based Segmentation of Medial Temporal Lobe Structures
Duchesne, S., Pruessner, J. C., & Collins, D. L. (2002) · NeuroImage · doi:10.1006/nimg.2002.1188 · Cited by 67 · see citing works
Automatic hippocampal segmentation in temporal lobe epilepsy: Impact of developmental abnormalities
Kim, H., Marie Chupin, A., Olivier Colliot, A., Bernhardt, B. C., Bernasconi, N., & Bernasconi, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.040 · Cited by 59 · see citing works
Assessing the reproducibility of the SienaX and Siena brain atrophy measures using the ADNI back-to-back MP-RAGE MRI scans
Cover, K. S., van Schijndel, R. A., van Dijk, B. W., Redolfi, A., Knol, D. L., Frisoni, G. B., Barkhof, F., & Vrenken, H. (2011) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2011.02.012 · Cited by 50 · see citing works
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Maltbie, E. A., Bhatt, K., Paniagua, B., Smith, R. G., Graves, M. M., Mosconi, M. W., Peterson, S. M., White, S., Blocher, J., El-Sayed, M., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.025 · Cited by 48 · see citing works
MR Imaging of Hippocampal Asymmetry at 3T in a Multiethnic, Population-Based Sample: Results from the Dallas Heart Study
Lucarelli, R. T., Peshock, R. M., McColl, R. W., Hulsey, K., Ayers, C. R., Whittemore, A. R., & King, K. S. (2012) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3308 · Cited by 47 · see citing works
Automatic calculation of hippocampal atrophy rates using a hippocampal template and the boundary shift integral
Barnes, J., Boyes, R. G., Lewis, E. B., Schott, J. M., Frost, C. D., Scahill, R. I., & Fox, N. C. (2006) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2006.07.008 · Cited by 46 · see citing works
Improved reliability of hippocampal atrophy rate measurement in mild cognitive impairment using fluid registration
van de Pol, L. A., Barnes, J., Scahill, R. I., Frost, C. D., Lewis, E. B., Boyes, R. G., van Schijndel, R. A., Scheltens, P., Fox, N. C., & Barkhof, F. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.10.033 · Cited by 41 · see citing works
Self-citation
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Takao, H., Abe, O., Hayashi, N., Kabasawa, H., & Ohtomo, K. (2010) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.22237 · Cited by 20 · see citing works

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de Vos, F., Schouten, T. M., Hafkemeijer, A., Dopper, E. G. P., Swieten, J. C. V., de Rooij, M., van der Grond, J., & Rombouts, S. A. R. B. (2016) · Human Brain Mapping · doi:10.1002/hbm.23147 · Cited by 68 · see citing works
Brain atrophy and white-matter hyperintensities are not significantly associated with incidence and severity of postoperative delirium in older persons without dementia
Cavallari, M., Hshieh, T. T., Guttmann, C. R. G., Ngo, L. H., Meier, D., Schmitt, E. M., Marcantonio, E. R., Jones, R. N., Kosar, C. M., Fong, T. G., et al. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.02.024 · Cited by 68 · see citing works
Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification
Næss‐Schmidt, E. T., Tietze, A., Blicher, J. U., Petersen, M. S., Mikkelsen, I. K., Pierrick Coupé, A., Manjón, J. V., & Eskildsen, S. F. (2016) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1433-0 · Cited by 63 · see citing works
Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI
Amiri, H., de Sitter, A., Bendfeldt, K., Battaglini, M., Wheeler-Kingshott, C. G. G., Calabrese, M., Geurts, J., Rocca, M., Sastre-Garriga, J., Enzinger, C., et al. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.04.023 · Cited by 56 · see citing works
Self-citation
Reproducibility of Brain MRI Segmentation Algorithms: Empirical Comparison of Local MAP PSTAPLE, FreeSurfer, and FSL‐FIRST
Velasco‐Annis, C., Akhondi‐Asl, A., Stamm, A., & Warfield, S. K. (2017) · Journal of Neuroimaging · doi:10.1111/jon.12483 · Cited by 55 · see citing works
Quantitative Neuroimaging Software for Clinical Assessment of Hippocampal Volumes on MR Imaging
Ahdidan, J., Raji, C. A., DeYoe, E. A., Mathis, J. R., Noe, K. Ø., Rimestad, J., Kjeldsen, T. K., Mosegaard, J., Becker, J. T., & López, O. L. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-150559 · Cited by 53 · see citing works
On brain atlas choice and automatic segmentation methods: a comparison of MAPER & FreeSurfer using three atlas databases
Yaakub, S. N., Heckemann, R. A., Keller, S. S., McGinnity, C. J., Weber, B., & Hammers, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-57951-6 · Cited by 55 · see citing works
White matter lesions and brain atrophy in systemic lupus erythematosus patients: correlation to cognitive dysfunction in a cohort of systemic lupus erythematosus patients using different definition models for neuropsychiatric systemic lupus erythematosus
Cannerfelt, B., Nystedt, J., Jönsen, A., Lätt, J., van Westen, D., Lilja, Å., Bengtsson, A. A., Nilsson, P., Mårtensson, J., & Sundgren, P. C. (2018) · Lupus · doi:10.1177/0961203318763533 · Cited by 53 · see citing works
Morphometric changes in the reward system of Parkinson’s disease patients with impulse control disorders
Pellicano, C., Niccolini, F., Wu, K., O’Sullivan, S. S., Lawrence, A. D., Lees, A. J., Piccini, P. P., & Politis, M. (2015) · Journal of Neurology · doi:10.1007/s00415-015-7892-3 · Cited by 50 · see citing works
Forecasting individual progression trajectories in Alzheimer’s disease
Maheux, E., Koval, I., Ortholand, J., Birkenbihl, C. J., Archetti, D., Bouteloup, V., Epelbaum, S., Dufouil, C., Hofmann‐Apitius, M., & Durrleman, S. (2023) · Nature Communications · doi:10.1038/s41467-022-35712-5 · Cited by 48 · see citing works
Age-related differences in the structural complexity of subcortical and ventricular structures
Madan, C. R., & Kensinger, E. A. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.10.023 · Cited by 48 · see citing works
A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies
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 · see citing works
Robustness of radiomics to variations in segmentation methods in multimodal brain MRI
Poirot, M. G., Caan, M. W. A., Ruhé, H. G., Bjørnerud, A., Groote, I. R., Reneman, L., & Marquering, H. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-20703-9 · Cited by 45 · see citing works
Impact of lifestyle dimensions on brain pathology and cognition
Schreiber, S., Vogel, J. W., Schwimmer, H., Marks, S. M., Schreiber, F., & Jagust, W. J. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.01.012 · Cited by 44 · see citing works
Amygdalar nuclei and hippocampal subfields on MRI: Test-retest reliability of automated volumetry across different MRI sites and vendors
Quattrini, G., Pievani, M., Jovicich, J., Aiello, M., Bargalló, N., Barkhof, F., Bartrés‐Faz, D., Beltramello, A., Pizzini, F. B., Blin, O., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116932 · Cited by 44 · see citing works
Self-citation
Reduced Hippocampal Volume and Its Relationship With Verbal Memory and Negative Symptoms in Treatment-Naive First-Episode Adolescent-Onset Schizophrenia
Duan, X., He, C., Ou, J., Wang, R., Xiao, J., Li, L., Wu, R., Zhang, Y., Zhao, J., & Chen, H. (2020) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa092 · Cited by 45 · see citing works
Sample Size Estimation for Alzheimer’s Disease Trials from Japanese ADNI Serial Magnetic Resonance Imaging
Fujishima, M., Kawaguchi, A., Maikusa, N., Kuwano, R., Iwatsubo, T., Matsuda, H., (ADNI), F. T. J. A. D. N. I., & (J-ADNI), T. J. A. D. N. I. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160621 · Cited by 40 · see citing works
Longitudinal volumetric evaluation of hippocampus and amygdala subregions in recent trauma survivors
Ben‐Zion, Z., Korem, N., Spiller, T. R., Duek, O. A., Keynan, J. N., Admon, R., Harpaz‐Rotem, I., Liberzon, I., Shalev, A. Y., & Hendler, T. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01842-x · Cited by 41 · see citing works
A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder
Fu, Y., Zhang, J., Li, Y., Shi, J., Zou, Y., Guo, H., Li, Y., Yao, Z., Wang, Y., & Hu, B. (2020) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2020.109989 · Cited by 38 · see citing works
A comparison of automated segmentation and manual tracing in estimating hippocampal volume in ischemic stroke and healthy control participants
Khlif, M. S., Egorova, N., Werden, E., Redolfi, A., Boccardi, M., DeCarli, C. S., Fletcher, E. M., Singh, B., Li, Q., Bird, L., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.10.019 · Cited by 35 · see citing works
The traveling heads: multicenter brain imaging at 7 Tesla
Voelker, M. N., Kraff, O., Brenner, D., Wollrab, A., Weinberger, O., Berger, M. C., Robinson, S., Bogner, W., Wiggins, C. J., Trampel, R., et al. (2016) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-016-0541-8 · Cited by 34 · see citing works
Reliability and sensitivity of two whole-brain segmentation approaches included in FreeSurfer – ASEG and SAMSEG
Sederevičius, D., Dídac Vidal-piñeiro, A., Sørensen, Ø., Leemput, K. V., Iglesias, J. E., Dalca, A. V., Greve, D. N., Fischl, B. R., Bjørnerud, A., Walhovd, K. B., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118113 · Cited by 33 · see citing works
Stability of hippocampal subfield volumes after trauma and relationship to development of PTSD symptoms
Weis, C., Webb, E. K., Huggins, A. A., Kallenbach, M. D., Miskovich, T. A., Fitzgerald, J. M., Bennett, K. P., Krukowski, J. L., deRoon‐Cassini, T. A., & Larson, C. L. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118076 · Cited by 30 · see citing works
Reproducibility of hippocampal atrophy rates measured with manual, FreeSurfer, AdaBoost, FSL/FIRST and the MAPS-HBSI methods in Alzheimer's disease
Cover, K. S., van Schijndel, R. A., Versteeg, A., Leung, K. K., Mulder, E. R., de Jong, R. A., Visser, P. J., Redolfi, A., Revillard, J., Grenier, B., et al. (2016) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2016.04.006 · Cited by 29 · see citing works
Self-citation
Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging
Vieira, B. H., Liem, F., Dadi, K., Engemann, D. A., Gramfort, A., Bellec, P., Craddock, R. C., Damoiseaux, J. S., Steele, C. J., Yarkoni, T., et al. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.06.008 · Cited by 28 · see citing works
Neuroanatomical Risk Factors for Posttraumatic Stress Disorder in Recent Trauma Survivors
Ben‐Zion, Z., Artzi, M., Niry, D., Keynan, J. N., Zeevi, Y., Admon, R., Sharon, H., Halpern, P., Liberzon, I., Shalev, A. Y., et al. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.11.003 · Cited by 27 · see citing works
COMT Val158Met polymorphism moderates the association between PTSD symptom severity and hippocampal volume
Hayes, J. P., Logue, M. W., Reagan, A., Salat, D. H., Wolf, E. J., Sadeh, N., Spielberg, J. M., Sperbeck, E., Hayes, S. M., McGlinchey, R. E., et al. (2017) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.150339 · Cited by 29 · see citing works
Photoperiod is associated with hippocampal volume in a large community sample
Miller, M. A., Leckie, R. L., Donofry, S. D., Gianaros, P. J., Erickson, K. I., Manuck, S. B., & Roecklein, K. A. (2014) · Hippocampus · doi:10.1002/hipo.22390 · Cited by 26 · see citing works
Your algorithm might think the hippocampus grows in Alzheimer's disease: Caveats of longitudinal automated hippocampal volumetry
Sankar, T., Park, M. T. M., Jawa, N. A., Patel, R., Bhagwat, N., Voineskos, A. N., Lozano, A. M., Chakravarty, M. M., & Initiative, T. A. D. N. (2017) · Human Brain Mapping · doi:10.1002/hbm.23559 · Cited by 26 · see citing works
BDNF genotype is associated with hippocampal volume in mild traumatic brain injury
Hayes, J. P., Reagan, A., Logue, M. W., Hayes, S. M., Sadeh, N., Miller, D. R., Verfaellie, M. H., Wolf, E. J., McGlinchey, R. E., Milberg, W., et al. (2017) · Genes Brain & Behavior · doi:10.1111/gbb.12403 · Cited by 26 · see citing works
Hippocampal formation alterations differently contribute to autobiographic memory deficits in mild cognitive impairment and Alzheimer's disease
Hirjak, D., Wolf, R. C., Remmele, B., Seidl, U., Thomann, A., Kubera, K. M., Schröder, J., Maier-Hein, K. H., & Thomann, P. A. (2017) · Hippocampus · doi:10.1002/hipo.22726 · Cited by 25 · see citing works
Validity of automated FreeSurfer segmentation compared to manual tracing in detecting prenatal alcohol exposure-related subcortical and corpus callosal alterations in 9- to 11-year-old children
Biffen, S. C., Warton, C. M. R., Dodge, N. C., Molteno, C. D., Jacobson, J. L., Jacobson, S. W., & Meintjes, E. M. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102368 · Cited by 25 · see citing works
Diagnostic performance of magnetic resonance imaging–based machine learning in Alzheimer’s disease detection: a meta-analysis
Hu, J., Wang, Y., Guo, D., Qu, Z., Sui, C., He, G., Wang, S., Chen, X., Wang, C., & Liu, X. (2022) · Neuroradiology · doi:10.1007/s00234-022-03098-2 · Cited by 24 · see citing works
Automatic Localization and Discrete Volume Measurements of Hippocampi From MRI Data Using a Convolutional Neural Network
Basher, A., Kim, B. C., Lee, K. H., & Jung, H. Y. (2020) · IEEE Access · doi:10.1109/access.2020.2994388 · Cited by 24 · see citing works
Subcortical and hippocampal brain segmentation in 5‐year‐old children: Validation of FSL‐FIRST and FreeSurfer against manual segmentation
Lidauer, K., Pulli, E. P., Copeland, A., Silver, E. A., Kumpulainen, V., Hashempour, N., Merisaari, H., Saunavaara, J. P., Parkkola, R. K., Lähdesmäki, T., et al. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15761 · Cited by 24 · see citing works
Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI
Shaw, T. B., York, A., Ziaei, M., Barth, M., Bollmann, S., & Initiative, F. T. A. D. N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116798 · Cited by 22 · see citing works
Perimenopausal hormone therapy is associated with regional sparing of the CA1 subfield: a HUNT MRI study
Pintzka, C. W. S., & Haberg, A. K. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.05.023 · Cited by 22 · see citing works
Inter-observer variation of hippocampus delineation in hippocampal avoidance prophylactic cranial irradiation
Bartel, F., van Herk, M., Vrenken, H., Vandaele, F., Sunaert, S. G., Jaeger, K. D., Dollekamp, N. J., Carbaat, C., Lamers, E., Dieleman, E. M. T., et al. (2018) · Clinical & Translational Oncology · doi:10.1007/s12094-018-1903-7 · Cited by 21 · see citing works
Self-citation
Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem
Wang, J. Y., Ngo, M. M., Hessl, D. R., Hagerman, R. J., & Rivera, S. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156123 · Cited by 21 · see citing works
Longitudinal changes in gray matter correspond to changes in cognition across the lifespan: implications for theories of cognition
Smith, E. T., Hennessee, J. P., Wig, G. S., Frank, S., González, H. M., Bacci, J. R., Chan, M. Y., Carreno, C. A., Kennedy, K. M., Rodrigue, K. M., et al. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.04.014 · Cited by 20 · see citing works
Hippocampal Complex Atrophy in Poststroke and Mild Cognitive Impairment
Selnes, P., Grambaitė, R., Rincón, M., Bjørnerud, A., Gjerstad, L., Hessen, E., Auning, E., Johansen, K. K., Almdahl, I. S., Due‐Tønnessen, P., et al. (2015) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2015.110 · Cited by 20 · see citing works
Comparison of automated brain volumetry methods with stereology in children aged 2 to 3 years
Heye, K., Latal, B., Knirsch, W., Scheer, I., von Rhein, M., Reich, B., Bauer, J. M., Gummel, K., Roberts, N., & Tuura, R. (2016) · Neuroradiology · doi:10.1007/s00234-016-1714-x · Cited by 19 · see citing works
Assessment of longitudinal hippocampal atrophy in the first year after ischemic stroke using automatic segmentation techniques
Khlif, M. S., Werden, E., Egorova, N., Boccardi, M., Redolfi, A., Bird, L., & Brodtmann, A. G. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102008 · Cited by 19 · see citing works
Reproducibility of Deep Gray Matter Atrophy Rate Measurement in a Large Multicenter Dataset
Meijerman, A., Amiri, H., Steenwijk, M. D., Jonker, M. A., van Schijndel, R. A., Cover, K. S., & Vrenken, H. (2017) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5459 · Cited by 16 · see citing works
Self-citation
Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results
Lyden, H. M., Gimbel, S. I., Piero, L. B. D., Tsai, A. B., Sachs, M. E., Kaplan, J., Margolin, G., & Saxbe, D. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00398 · Cited by 15 · see citing works
Braak neurofibrillary tangle staging prediction from in vivo MRI metrics
Dallaire-Théroux, C., Beheshti, I., Potvin, O., Dieumegarde, L., Saikali, S., & Duchesne, S. (2019) · Alzheimer's & Dementia · doi:10.1016/j.dadm.2019.07.001 · Cited by 12 · see citing works
Regional analysis of volumes and reproducibilities of automatic and manual hippocampal segmentations
Bartel, F., Vrenken, H., Bijma, F., Barkhof, F., van Herk, M., & de Munck, J. C. (2017) · PLoS ONE · doi:10.1371/journal.pone.0166785 · Cited by 15 · see citing works
Self-citation
Structural Alteration of Medial Temporal Lobe Subfield in the Amnestic Mild Cognitive Impairment Stage of Alzheimer’s Disease
He, P., Qu, H., Cai, M., Liu, W., Gu, X., & Ma, Q. (2022) · Neural Plasticity · doi:10.1155/2022/8461235 · Cited by 15 · see citing works
Comparative Utility of Manual versus Automated Segmentation of Hippocampus and Entorhinal Cortex Volumes in a Memory Clinic Sample
Fung, Y. L., Ng, K. E. T., Vogrin, S. J., Meade, C. E., Ngo, M. M., Collins, S. J., & Bowden, S. C. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-181172 · Cited by 15 · see citing works
Alterations of Graphic Properties and Related Cognitive Functioning Changes in Mild Alzheimer’s Disease Revealed by Individual Morphological Brain Network
Li, W., Yang, C., Wu, S., Nie, Y., Zhang, X., Lu, M., Chu, T., & Shi, F. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00927 · Cited by 13 · see citing works
Automatic Thalamus Segmentation on Unenhanced 3D T1 Weighted Images: Comparison of Publicly Available Segmentation Methods in a Pediatric Population
Hannoun, S., Tutunji, R., Homsi, M. E., Saaybi, S. R., & Hourani, R. G. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9408-7 · Cited by 14 · see citing works
Evaluating brain volume segmentation accuracy and reliability of FreeSurfer and Neurophet AQUA at variations in MRI magnetic field strengths
Lee, H., Kim, H. W., Lee, M., Kang, J., Kim, D., Lim, H. K., Lee, J., Kim, E., & Kim, R. E. Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-74622-y · Cited by 13 · see citing works
Accuracy of automated amygdala MRI segmentation approaches in Huntington's disease in the IMAGE‐HD cohort
Alexander, B., Georgiou‐Karistianis, N., Beare, R. J., Ahveninen, L. M., Lorenzetti, V., Stout, J. C., & Glikmann‐Johnston, Y. (2020) · Human Brain Mapping · doi:10.1002/hbm.24918 · Cited by 12 · see citing works
Can measuring hippocampal atrophy with a fully automatic method be substantially less noisy than manual segmentation over both 1 and 3 years?
Cover, K. S., van Schijndel, R. A., Bosco, P., Damangir, S., & Redolfi, A. (2018) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2018.06.011 · Cited by 13 · see citing works
Self-citation
FAst Segmentation Through SURface Fairing (FASTSURF): A novel semi-automatic hippocampus segmentation method
Bartel, F., Vrenken, H., van Herk, M., de Ruiter, M. B., Belderbos, J. S. A., Hulshof, J., & de Munck, J. C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210641 · Cited by 13 · see citing works
Self-citation
Simultaneous PET-MRI Studies of the Concordance of Atrophy and Hypometabolism in Syndromic Variants of Alzheimer’s Disease and Frontotemporal Dementia: An Extended Case Series
Moodley, K. K., Perani, D., Minati, L., Rosa, P. A. D., Pennycook, F., Dickson, J. C., Barnes, A., Contarino, V. E., Michopoulou, S. K., D’Incerti, L., et al. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-150151 · Cited by 13 · see citing works
Exploring the multidimensional nature of repetitive and restricted behaviors and interests (RRBI) in autism: neuroanatomical correlates and clinical implications
Lefebvre, A., Traut, N., Pedoux, A., Maruani, A., Beggiato, A., Elmaleh, M., Germanaud, D., Amestoy, A., Moal, M. L., Chatham, C., et al. (2023) · Molecular Autism · doi:10.1186/s13229-023-00576-z · Cited by 12 · see citing works
Hippocampal volume and integrity as predictors of cognitive decline in intact elderly
Bruno, D., Ciarleglio, A. J., Grothe, M., Nierenberg, J., Bachman, A., Teipel, S., Petkova, E., Ardekani, B., & Pomara, N. (2016) · NeuroReport · doi:10.1097/wnr.0000000000000629 · Cited by 11 · see citing works
Non-linear realignment improves hippocampus subfield segmentation reliability
Shaw, T. B., Bollmann, S., Atcheson, N., Strike, L. T., Guo, C. C., McMahon, K. L., Fripp, J., Wright, M. J., Salvado, O., & Barth, M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116206 · Cited by 12 · see citing works
The Parietal Atrophy Score on Brain Magnetic Resonance Imaging is a Reliable Visual Scale
Šilhán, D., Bartoš, A., Mrzílková, J., Pashkovska, O., Ibrahim, I., & Tintěra, J. (2020) · Current Alzheimer Research · doi:10.2174/1567205017666200807193957 · Cited by 12 · see citing works
Prediction of Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using Amyloid PET and Brain MR Imaging Data: A 48-Month Follow-Up Analysis of the Alzheimer’s Disease Neuroimaging Initiative Cohort
Kim, D.-H., Oh, M., & Kim, J. S. (2023) · Diagnostics · doi:10.3390/diagnostics13213375 · Cited by 12 · see citing works
Will PET Amyloid Imaging Lead to Overdiagnosis of Alzheimer Dementia?
Dubroff, J. G., & Nasrallah, I. M. (2015) · Academic Radiology · doi:10.1016/j.acra.2015.02.005 · Cited by 11 · see citing works
Patients with Amyloid-Negative Mild Cognitive Impairment have Cortical Hypometabolism but the Hippocampus is Preserved
Hanseeuw, B. J., Dricot, L., Lhommel, R., Quenon, L., & Ivanoiu, A. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160204 · Cited by 11 · see citing works
Longitudinal Assessment of Hippocampal Atrophy in Midlife and Early Old Age: Contrasting Manual Tracing and Semi-automated Segmentation (FreeSurfer)
Fraser, M. A., Shaw, M. E., Anstey, K. J., & Cherbuin, N. (2018) · Brain Topography · doi:10.1007/s10548-018-0659-2 · Cited by 11 · see citing works
Expert Opinions and Recommendations for the Clinical Use of Quantitative Analysis Software for MRI-Based Brain Volumetry
Lee, J. Y., Park, J. E., Chung, M. S., Oh, S. W., Moon, W., & Aging and Neurodegeneration Imaging (ANDI) Study Group, K. S. O. N. (. (2021) · Journal of the Korean Society of Radiology · doi:10.3348/jksr.2020.0174 · Cited by 11 · see citing works
Non-linear registration improves statistical power to detect hippocampal atrophy in aging and dementia
Bartel, F., Visser, M., de Ruiter, M. B., Belderbos, J. S. A., Barkhof, F., Vrenken, H., de Munck, J. C., & van Herk, M. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101902 · Cited by 10 · see citing works
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Effects of sex, age, and apolipoprotein E genotype on hippocampal parenchymal fraction in cognitively normal older adults
Ardekani, B. A., Izadi, N. O., Hadid, S. A., Meftah, A. M., & Bachman, A. H. (2020) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2020.111107 · Cited by 10 · see citing works
Hippocampo-Horn Percentage and Parietal Atrophy Score for Easy Visual Assessment of Brain Atrophy on Magnetic Resonance Imaging in Early- and Late-Onset Alzheimer’s Disease
Šilhán, D., Initiative, A. D. N., Pashkovska, O., & Bartoš, A. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210372 · Cited by 10 · see citing works
The relevance of hippocampal subfield integrity and clock drawing test performance for the diagnosis of Alzheimer’s disease and mild cognitive impairment
Hirjak, D., Sambataro, F., Remmele, B., Kubera, K. M., Schröder, J., Seidl, U., Thomann, A., Maier-Hein, K. H., Wolf, R. C., & Thomann, P. A. (2017) · The World Journal of Biological Psychiatry · doi:10.1080/15622975.2017.1355474 · Cited by 10 · see citing works
Microstructural changes in the brain after long‐term post‐concussion symptoms: A randomized trial
Næss‐Schmidt, E. T., Blicher, J. U., Thastum, M. M., Rask, C. U., Svendsen, S. W., Schröder, A., Tuborgh, A. H., Østergaard, L., Sangill, R., Lund, T. E., et al. (2020) · Journal of Neuroscience Research · doi:10.1002/jnr.24773 · Cited by 9 · see citing works
Agreement Between Automated and Manual MRI Volumetry in Alzheimer's Disease: A Systematic Review and Meta‐Analysis
Quek, Y., Fung, Y. L., Cheung, M. W. ‐., Vogrin, S. J., Collins, S. J., & Bowden, S. C. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28037 · Cited by 8 · see citing works
Survey of Neuroanatomic Sampling and Staining Procedures in Alzheimer Disease Research Center Brain Banks
Vizcarra, J. C., Teich, A. F., Dugger, B. N., & Gutman, D. A. (2023) · Free Neuropathology · doi:10.17879/freeneuropathology-2023-4696 · Cited by 8 · see citing works
Discrete pre-processing step effects in registration-based pipelines, a preliminary volumetric study on T1-weighted images
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Sánchez‐Benavides, G., Gómez‐Ansón, B., Sainz, A., Vives‐Gilabert, Y., Delfino, M., & Peña‐Casanova, J. (2010) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2009.10.011 · Cited by 109 · see citing works
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Authors: Mulder, E. R., de Jong, R. A., Knol, D. L., van Schijndel, R. A., Cover, K. S., Visser, P. J., Barkhof, F., & Vrenken, H.
Affiliations: Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
Gemeente Amsterdam (NL)
Corresponding author: Vrenken, Hugo
ORCID: Mulder, Emma R.
Cover, Keith S.
Visser, Pieter J.
Barkhof, Frederik
Vrenken, Hugo
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2014-05
Volume: 92
Pages: 169-181
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2014.01.058
OpenAlex: W1981789870
Cited by: 122
References: 52
Citations by year: 2026: 4 · 2025: 2 · 2024: 4 · 2023: 8 · 2022: 9 · 2021: 8 · 2020: 14 · 2019: 12 · 2018: 11 · 2017: 18 · 2016: 16 · 2015: 14 · 2014: 2
FWCI: 12.73
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Research Publications (Maastricht University) (publishedVersion)
Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
UCL Discovery (University College London) (submittedVersion)
Pure Amsterdam UMC (submittedVersion)
VU Research Portal (submittedVersion)
Research Publications (Maastricht University) (submittedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion)
Funders: Bristol-Myers Squibb
Seventh Framework Programme (283562)
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
Keywords: hippocampal atrophy, reproducibility, FreeSurfer, FIRST, Alzheimer's disease, mild cognitive impairment, healthy aging, limits of agreement, longitudinal MRI, MPRAGE, segmentation errors, measurement uncertainty, ADNI cohort
MeSH terms: Aged, Algorithms, Alzheimer Disease, Artificial Intelligence, Atrophy, Female, Hippocampus, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Organ Size, Pattern Recognition, Automated, Sensitivity and Specificity, Software, Software Validation, Reproducibility of Results, Observer Variation, Imaging, Three-Dimensional, Cognitive Dysfunction

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Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST

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