{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T23:51:29Z","timestamp":1781221889487,"version":"3.54.1"},"reference-count":99,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T00:00:00Z","timestamp":1642982400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"BMBF Bonn","doi-asserted-by":"publisher","award":["031L0206"],"award-info":[{"award-number":["031L0206"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"BMBF Bonn","doi-asserted-by":"publisher","award":["01GQ1801"],"award-info":[{"award-number":["01GQ1801"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000005","name":"U.S. Department of Defense","doi-asserted-by":"publisher","award":["W81XWH-12-2-0012"],"award-info":[{"award-number":["W81XWH-12-2-0012"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 LM012719"],"award-info":[{"award-number":["R01 LM012719"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R56 MH121426"],"award-info":[{"award-number":["R56 MH121426"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P41 EB030006"],"award-info":[{"award-number":["P41 EB030006"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 AG064027"],"award-info":[{"award-number":["R01 AG064027"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01 AG024904"],"award-info":[{"award-number":["U01 AG024904"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["1U54MH091657"],"award-info":[{"award-number":["1U54MH091657"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004330","name":"GlaxoSmithKline","doi-asserted-by":"publisher","award":["6GKC"],"award-info":[{"award-number":["6GKC"]}],"id":[{"id":"10.13039\/100004330","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009607","name":"McDonnell Center for Systems Neuroscience","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017506","name":"Alzheimer\u2019s Society","doi-asserted-by":"publisher","award":["RF116"],"award-info":[{"award-number":["RF116"]}],"id":[{"id":"10.13039\/501100017506","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005224","name":"Deutsches Zentrum f\u00fcr Neurodegenerative Erkrankungen","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005224","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000135","name":"NIH Blueprint for Neuroscience Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000135","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007333","name":"Alzheimer&apos;s Disease Neuroimaging Initiative","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007333","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["NeuroImage"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1016\/j.neuroimage.2022.118933","type":"journal-article","created":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T23:32:00Z","timestamp":1643844720000},"page":"118933","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":82,"special_numbering":"C","title":["FastSurferVINN: Building resolution-independence into deep learning segmentation methods\u2014A solution for HighRes brain MRI"],"prefix":"10.1016","volume":"251","author":[{"given":"Leonie","family":"Henschel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"K\u00fcgler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Reuter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.neuroimage.2022.118933_sbref0001","doi-asserted-by":"crossref","first-page":"180308","DOI":"10.1038\/sdata.2018.308","article-title":"-Body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults","volume":"6","author":"A mind-brain","year":"2019","journal-title":"Sci. Data"},{"issue":"2","key":"10.1016\/j.neuroimage.2022.118933_bib0002","doi-asserted-by":"crossref","first-page":"101","DOI":"10.33851\/JMIS.2021.8.2.101","article-title":"Efficient multi-scalable network for single image super resolution","volume":"8","author":"Alao","year":"2021","journal-title":"J. Multimed. Inf. Syst."},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0003","series-title":"Handbook of Image and Video Processing","article-title":"7.1 - image scanning, sampling, and interpolation","author":"Allebach","year":"2005"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0004","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.neuroimage.2013.03.077","article-title":"A computational framework for ultra-high resolution cortical segmentation at 7\u00a0Tesla","volume":"93","author":"Bazin","year":"2014","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.neuroimage.2022.118933_sbref0005","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc."},{"key":"10.1016\/j.neuroimage.2022.118933_bib0006","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","first-page":"177","article-title":"Partial volume segmentation of brain MRI scans of any resolution and contrast","author":"Billot","year":"2020"},{"issue":"4","key":"10.1016\/j.neuroimage.2022.118933_bib0007","first-page":"P92","article-title":"Mri in the rhineland study: a novel protocol for population neuroimaging","volume":"10","author":"Breteler","year":"2014","journal-title":"Alzheimer\u2019s Dementia"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0008","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.neuroimage.2017.04.041","article-title":"Voxresnet: deep voxelwise residual networks for brain segmentation from 3D MR images","volume":"170","author":"Chen","year":"2018","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0009","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"3640","article-title":"Attention to scale: scale-aware semantic image segmentation","author":"Chen","year":"2016"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0010","series-title":"CVPR","first-page":"2248","article-title":"Blending-target domain adaptation by adversarial meta-adaptation networks","author":"Chen","year":"2019"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0011","doi-asserted-by":"crossref","first-page":"117026","DOI":"10.1016\/j.neuroimage.2020.117026","article-title":"Assemblynet: a large ensemble of CNNs for 3D whole brain MRI segmentation","volume":"219","author":"Coup\u00e9","year":"2020","journal-title":"NeuroImage"},{"issue":"3","key":"10.1016\/j.neuroimage.2022.118933_bib0012","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","article-title":"An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest","volume":"31","author":"Desikan","year":"2006","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0013","doi-asserted-by":"crossref","first-page":"170010","DOI":"10.1038\/sdata.2017.10","article-title":"Enhancing studies of the connectome in autism using the autism brain imaging data exchange II","volume":"4","author":"Di Martino","year":"2017","journal-title":"Sci. Data"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0014","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1038\/mp.2013.78","article-title":"The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism","volume":"19","author":"Di Martino","year":"2013","journal-title":"Mol. Psychiatry"},{"issue":"3","key":"10.1016\/j.neuroimage.2022.118933_sbref0015","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the amount of ecologic association between species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"issue":"2","key":"10.1016\/j.neuroimage.2022.118933_bib0016","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.neuroimage.2022.118933_bib0017","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","article-title":"Freesurfer","volume":"62","author":"Fischl","year":"2012","journal-title":"NeuroImage"},{"issue":"3","key":"10.1016\/j.neuroimage.2022.118933_bib0018","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/S0896-6273(02)00569-X","article-title":"Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain","volume":"33","author":"Fischl","year":"2002","journal-title":"Neuron"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0019","series-title":"Statistical Parametric Mapping: The Analysis of Functional Brain Images","author":"Friston","year":"2007"},{"issue":"7","key":"10.1016\/j.neuroimage.2022.118933_bib0020","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","article-title":"Joint optic disc and cup segmentation based on multi-label deep network and polar transformation","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0021","unstructured":"Gaser, C., Dahnke, R., 2016. Cat-a computational anatomy toolbox for the analysis of structural MRI data."},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0022","doi-asserted-by":"crossref","first-page":"101592","DOI":"10.1016\/j.media.2019.101592","article-title":"Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species","volume":"60","author":"Gerard","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2022.118933_bib0023","doi-asserted-by":"crossref","first-page":"3993","DOI":"10.1109\/TIP.2019.2963389","article-title":"Unsupervised multi-target domain adaptation: an information theoretic approach","volume":"29","author":"Gholami","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0024","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.neuroimage.2013.04.127","article-title":"The minimal preprocessing pipelines for the human connectome project","volume":"80","author":"Glasser","year":"2013","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0025","series-title":"Proceedings of the 30th International Conference on International Conference on Machine Learning-Volume 28","first-page":"III","article-title":"Maxout networks","author":"Goodfellow","year":"2013"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0026","series-title":"Computational Pathology and Ophthalmic Medical Image Analysis","first-page":"11","article-title":"Multi-resolution networks for semantic segmentation in whole slide images","author":"Gu","year":"2018"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0027","series-title":"Proceedings of the 2018\u00a0Conference on Empirical Methods in Natural Language Processing","first-page":"4694","article-title":"Multi-source domain adaptation with mixture of experts","author":"Guo","year":"2018"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0028","doi-asserted-by":"crossref","first-page":"117012","DOI":"10.1016\/j.neuroimage.2020.117012","article-title":"FastSurfer - a fast and accurate deep learning based neuroimaging pipeline","volume":"219","author":"Henschel","year":"2020","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0029","series-title":"Proceedings of the 35th International Conference on Machine Learning","first-page":"1989","article-title":"CyCADA: cycle-consistent adversarial domain adaptation","author":"Hoffman","year":"2018"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0030","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1097\/00004728-199803000-00032","article-title":"Enhancement of MR images using registration for signal averaging","volume":"22","author":"Holmes","year":"1998","journal-title":"J. Comput. Assist. Tomogr."},{"key":"10.1016\/j.neuroimage.2022.118933_bib0031","doi-asserted-by":"crossref","first-page":"136032","DOI":"10.1109\/ACCESS.2021.3115963","article-title":"Srnet: scale-aware representation learning network for dense crowd counting","volume":"9","author":"Huang","year":"2021","journal-title":"IEEE Access"},{"issue":"7","key":"10.1016\/j.neuroimage.2022.118933_bib0032","doi-asserted-by":"crossref","DOI":"10.1093\/gigascience\/giy082","article-title":"Nighres: processing tools for high-resolution neuroimaging","volume":"7","author":"Huntenburg","year":"2018","journal-title":"GigaScience"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0033","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.neuroimage.2019.03.041","article-title":"3D whole brain segmentation using spatially localized atlas network tiles","volume":"194","author":"Huo","year":"2019","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0034","doi-asserted-by":"crossref","first-page":"118206","DOI":"10.1016\/j.neuroimage.2021.118206","article-title":"Joint super-resolution and synthesis of 1\u00a0mm isotropic MP-rage volumes from clinical MRI exams with scans of different orientation, resolution and contrast","volume":"237","author":"Iglesias","year":"2021","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0035","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.neunet.2019.03.014","article-title":"Semi-supervised deep learning of brain tissue segmentation","volume":"116","author":"Ito","year":"2019","journal-title":"Neural Netw."},{"key":"10.1016\/j.neuroimage.2022.118933_bib0036","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/jmri.21049","article-title":"The alzheimer\u2019s disease neuroimaging initiative (ADNI): mri methods","volume":"27","author":"Jack","year":"2008","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0037","series-title":"Advances in Neural Information Processing Systems","article-title":"Spatial transformer networks","author":"Jaderberg","year":"2015"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0038","series-title":"Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on","first-page":"1175","article-title":"The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation","author":"J\u00e9gou","year":"2017"},{"issue":"2","key":"10.1016\/j.neuroimage.2022.118933_bib0039","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","article-title":"Fsl","volume":"62","author":"Jenkinson","year":"2012","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0040","series-title":"Computer Vision \u2013 ECCV 2020","first-page":"464","article-title":"Minimum class confusion for versatile domain adaptation","author":"Jin","year":"2020"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0041","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRFfor accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2022.118933_bib0042","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"1646","article-title":"Accurate image super-resolution using very deep convolutional networks","author":"Kim","year":"2016"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0043","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"1637","article-title":"Deeply-recursive convolutional network for image super-resolution","author":"Kim","year":"2016"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0044","doi-asserted-by":"crossref","first-page":"171","DOI":"10.3389\/fnins.2012.00171","article-title":"101 labeled brain images and a consistent human cortical labeling protocol","volume":"6","author":"Klein","year":"2012","journal-title":"Front. Neurosci."},{"issue":"4","key":"10.1016\/j.neuroimage.2022.118933_bib0045","doi-asserted-by":"crossref","first-page":"2854","DOI":"10.1016\/j.neuroimage.2010.11.047","article-title":"Multi-parametric neuroimaging reproducibility: a 3-Tresource study","volume":"54","author":"Landman","year":"2011","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0046","series-title":"2019 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"6053","article-title":"Scale-aware trident networks for object detection","author":"Li","year":"2019"},{"issue":"3","key":"10.1016\/j.neuroimage.2022.118933_sbref0047","doi-asserted-by":"crossref","DOI":"10.3390\/a13030060","article-title":"Mdan-UNet: multi-scale and dual attention enhanced nested U-Net architecture for segmentation of optical coherence tomography images","volume":"13","author":"Liu","year":"2020","journal-title":"Algorithms"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0048","series-title":"International Conference on Learning Representations","article-title":"SGDR: stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2017"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0049","series-title":"International Conference on Learning Representations","article-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2019"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0050","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.neuroimage.2012.12.016","article-title":"Cortical thickness determination of the human brain using high resolution 3T and 7T MRI data","volume":"70","author":"Luesebrink","year":"2013","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0051","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.neuroimage.2012.12.044","article-title":"Miriad-public release of a multiple time point Alzheimer\u2019s MR imaging dataset","volume":"70","author":"Malone","year":"2013","journal-title":"NeuroImage"},{"issue":"12","key":"10.1016\/j.neuroimage.2022.118933_bib0052","doi-asserted-by":"crossref","first-page":"2677","DOI":"10.1162\/jocn.2009.21407","article-title":"Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults","volume":"22","author":"Marcus","year":"2010","journal-title":"J. Cogn. Neurosci."},{"issue":"9","key":"10.1016\/j.neuroimage.2022.118933_bib0053","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","article-title":"Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults","volume":"19","author":"Marcus","year":"2007","journal-title":"J. Cogn. Neurosci."},{"key":"10.1016\/j.neuroimage.2022.118933_bib0054","doi-asserted-by":"crossref","unstructured":"Markiewicz, C. J., Gorgolewski, K. J., Feingold, F., Blair, R., Halchenko, Y. O., Miller, E., Hardcastle, N., Wexler, J., Esteban, O., Goncalves, M., Jwa, A., Poldrack, R. A., 2021. OpenNeuro: an open resource for sharing of neuroimaging data10.1101\/2021.06.28.450168","DOI":"10.1101\/2021.06.28.450168"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0055","doi-asserted-by":"crossref","first-page":"67","DOI":"10.3389\/fninf.2019.00067","article-title":"Knowing what you know in brain segmentation using Bayesian deep neural networks","volume":"13","author":"McClure","year":"2019","journal-title":"Front. Neuroinform."},{"issue":"2","key":"10.1016\/j.neuroimage.2022.118933_bib0056","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JMI.4.2.024003","article-title":"BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures","volume":"4","author":"Mehta","year":"2017","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0057","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1111\/epi.12464","article-title":"3T MRI improves the detection of transmantle sign in type 2 focal cortical dysplasia","volume":"55","author":"Mellerio","year":"2014","journal-title":"Epilepsia"},{"issue":"1","key":"10.1016\/j.neuroimage.2022.118933_sbref0058","doi-asserted-by":"crossref","first-page":"180307","DOI":"10.1038\/sdata.2018.307","article-title":"A functional connectome phenotyping dataset including cognitive state and personality measures","volume":"6","author":"Mendes","year":"2019","journal-title":"Sci. Data"},{"issue":"8","key":"10.1016\/j.neuroimage.2022.118933_bib0059","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0006660","article-title":"Integrating functional and diffusion magnetic resonance imaging for analysis of structure-function relationship in the human language network","volume":"4","author":"Morgan","year":"2009","journal-title":"PLoS One"},{"issue":"1","key":"10.1016\/j.neuroimage.2022.118933_bib0060","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jalz.2005.06.003","article-title":"Ways toward an early diagnosis in Alzheimer\u2019s disease: the Alzheimer\u2019s disease neuroimaging initiative (ADNI)","volume":"1","author":"Mueller","year":"2005","journal-title":"Alzheimer\u2019s Dementia"},{"issue":"1","key":"10.1016\/j.neuroimage.2022.118933_bib0061","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/TMI.1983.4307610","article-title":"Comparison of interpolating methods for image resampling","volume":"2","author":"Parker","year":"1983","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0062","series-title":"NIPS Workshop Autodiff","article-title":"Automatic differentiation in Pytorch","author":"Paszke","year":"2017"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0063","series-title":"2019\u00a0IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"1406","article-title":"Moment matching for multi-source domain adaptation","author":"Peng","year":"2019"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0064","doi-asserted-by":"crossref","first-page":"12","DOI":"10.3389\/fninf.2013.00012","article-title":"Toward open sharing of task-based fMRI data: the openfmri project","volume":"7","author":"Poldrack","year":"2013","journal-title":"Front. Neuroinform."},{"key":"10.1016\/j.neuroimage.2022.118933_bib0065","doi-asserted-by":"crossref","first-page":"160110","DOI":"10.1038\/sdata.2016.110","article-title":"A phenome-wide examination of neural and cognitive function","volume":"3","author":"Poldrack","year":"2016","journal-title":"Sci. Data"},{"issue":"11","key":"10.1016\/j.neuroimage.2022.118933_bib0066","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1038\/nn.3818","article-title":"Making big data open: data sharing in neuroimaging","volume":"17","author":"Poldrack","year":"2014","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0067","series-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2018","first-page":"603","article-title":"Autofocus layer for semantic segmentation","volume":"vol. 11072","author":"Qin","year":"2018"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0068","series-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2015","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","volume":"9351","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0069","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1016\/j.neuroimage.2018.11.042","article-title":"Quicknat: a fully convolutional network for quick and accurate segmentation of neuroanatomy","volume":"186","author":"Roy","year":"2019","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0070","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"231","article-title":"Error corrective boosting for learning fully convolutional networks with limited data","author":"Roy","year":"2017"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0071","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"5351","article-title":"Curriculum graph co-teaching for multi-target domain adaptation","author":"Roy","year":"2021"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0072","series-title":"Image Analysis and Processing \u2013 ICIAP 2019","first-page":"292","article-title":"Towards multi-source adaptive semantic segmentation","author":"Russo","year":"2019"},{"issue":"6","key":"10.1016\/j.neuroimage.2022.118933_bib0073","first-page":"464","article-title":"Theory and design of local interpolators","volume":"55","author":"Schaum","year":"1993","journal-title":"CVGIP"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0074","doi-asserted-by":"crossref","unstructured":"Shen, J., Wang, Y., Zhang, J., 2021. ASDN: a deep convolutional network for arbitrary scale image super-resolution 26 (1), 13\u201326. 10.1007\/s11036-020-01720-2","DOI":"10.1007\/s11036-020-01720-2"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0075","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1016\/j.neuroimage.2011.06.047","article-title":"Parcellation of human amygdala in vivo using ultra high field structural MRI","volume":"58","author":"Solano-Castiella","year":"2011","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0076","doi-asserted-by":"crossref","first-page":"e50","DOI":"10.1111\/j.1552-6569.2009.00449.x","article-title":"Brain MRI lesion load at 1.5T and 3T versus clinical status in multiple sclerosis","volume":"21","author":"Stankiewicz","year":"2011","journal-title":"J. Neuroimaging"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0077","first-page":"1","article-title":"A 3D spatially-weighted network for segmentation of brain tissue from MRI","author":"Sun","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0078","first-page":"1","article-title":"A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on danish commons","volume":"5","author":"S\u00f8rensen","year":"1948"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0079","series-title":"Handbook of Medical Image Processing and Analysis","first-page":"465","article-title":"Image interpolation and resampling","author":"Thevenaz","year":"2009"},{"issue":"5","key":"10.1016\/j.neuroimage.2022.118933_sbref0080","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.ejrad.2011.07.007","article-title":"Clinical applications of 7T MRI in the brain","volume":"82","author":"van der Kolk","year":"2013","journal-title":"Eur. J. Radiol."},{"issue":"4","key":"10.1016\/j.neuroimage.2022.118933_bib0081","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1016\/j.neuroimage.2012.02.018","article-title":"The human connectome project: a data acquisition perspective","volume":"62","author":"Van Essen","year":"2012","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0082","doi-asserted-by":"crossref","first-page":"101890","DOI":"10.1016\/j.media.2020.101890","article-title":"Hooknet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images","volume":"68","author":"van Rijthoven","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2022.118933_bib0083","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.neuroimage.2017.02.035","article-title":"Deepnat: deep convolutional neural network for segmenting neuroanatomy","volume":"170","author":"Wachinger","year":"2018","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0084","series-title":"2019\u00a0IEEE International Conference on Data Mining (ICDM)","first-page":"1372","article-title":"TMDA: task-specific multi-source domain adaptation via clustering embedded adversarial training","author":"Wang","year":"2019"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0085","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.compbiomed.2017.03.024","article-title":"A multi-resolution approach for spinal metastasis detection using deep siamese neural networks","volume":"84","author":"Wang","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0086","first-page":"1794","article-title":"Does high-field MR imaging have an influence on the classification of patients with clinically isolated syndromes according to current diagnostic MR imaging criteria for multiple sclerosis?","volume":"27","author":"Wattjes","year":"2006","journal-title":"Am. J. Neuroradiol. AJNR"},{"issue":"6","key":"10.1016\/j.neuroimage.2022.118933_sbref0087","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","article-title":"Individual comparisons by ranking methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Biom. Bull."},{"issue":"1","key":"10.1016\/j.neuroimage.2022.118933_bib0088","doi-asserted-by":"crossref","DOI":"10.1186\/s12883-020-01874-2","article-title":"The TRACK-PD study: protocol of a longitudinal ultra-high field imaging study in Parkinson\u2019s disease","volume":"20","author":"Wolters","year":"2020","journal-title":"BMC Neurol."},{"key":"10.1016\/j.neuroimage.2022.118933_bib0089","series-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"3964","article-title":"Deep cocktail network: multi-source unsupervised domain adaptation with category shift","author":"Xu","year":"2018"},{"issue":"1","key":"10.1016\/j.neuroimage.2022.118933_sbref0090","article-title":"Hrcnet: high-resolution context extraction network for semantic segmentation of remote sensing images","volume":"13","author":"Xu","year":"2021","journal-title":"Remote Sens."},{"key":"10.1016\/j.neuroimage.2022.118933_bib0091","series-title":"Advances in Multimedia Information Processing \u2013 PCM 2018","first-page":"232","article-title":"Attention to refine through multi scales for semantic segmentation","author":"Yang","year":"2018"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0092","article-title":"Heterogeneous graph attention network for unsupervised multiple-target domain adaptation","volume":"PP","author":"Yang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.neuroimage.2022.118933_bib0093","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1002\/hbm.22627","article-title":"Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment","volume":"36","author":"Yushkevich","year":"2014","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.neuroimage.2022.118933_bib0094","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neuroimage.2017.09.060","article-title":"Advantages of cortical surface reconstruction using submillimeter 7\u00a0T MEMPRAGE","volume":"165","author":"Zaretskaya","year":"2018","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.neuroimage.2022.118933_bib0095","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/42.906424","article-title":"Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm","volume":"20","author":"Zhang","year":"2001","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2022.118933_sbref0096","series-title":"Advances in Neural Information Processing Systems","article-title":"Multi-source domain adaptation for semantic segmentation","author":"Zhao","year":"2019"},{"key":"10.1016\/j.neuroimage.2022.118933_bib0097","doi-asserted-by":"crossref","first-page":"4530","DOI":"10.1109\/JSTARS.2021.3071353","article-title":"Integrating gate and attention modules for high-resolution image semantic segmentation","volume":"14","author":"Zheng","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.neuroimage.2022.118933_bib0098","first-page":"5989","article-title":"Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources","volume":"33","author":"Zhu","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"9","key":"10.1016\/j.neuroimage.2022.118933_sbref0099","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1002\/nbm.3275","article-title":"Recent applications of UHF-MRI in the study of human brain function and structure: a review","volume":"29","author":"van der Zwaag","year":"2016","journal-title":"NMR Biomed."}],"container-title":["NeuroImage"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811922000623?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811922000623?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T03:45:27Z","timestamp":1777607127000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1053811922000623"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5]]},"references-count":99,"alternative-id":["S1053811922000623"],"URL":"https:\/\/doi.org\/10.1016\/j.neuroimage.2022.118933","relation":{},"ISSN":["1053-8119"],"issn-type":[{"value":"1053-8119","type":"print"}],"subject":[],"published":{"date-parts":[[2022,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FastSurferVINN: Building resolution-independence into deep learning segmentation methods\u2014A solution for HighRes brain MRI","name":"articletitle","label":"Article Title"},{"value":"NeuroImage","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neuroimage.2022.118933","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Author(s). Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"118933"}}