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Depositordc.contributorValdés Hernández, Maria
Funderdc.contributor.otherOtheren_UK
Spatial Coveragedc.coverage.spatialUKen
Spatial Coveragedc.coverage.spatialUNITED KINGDOMen
Spatial Coveragedc.coverage.spatialCAen
Spatial Coveragedc.coverage.spatialCANADAen
Spatial Coveragedc.coverage.spatialIEen
Spatial Coveragedc.coverage.spatialIRELANDen
Spatial Coveragedc.coverage.spatialUSen
Spatial Coveragedc.coverage.spatialUNITED STATESen
Time Perioddc.coverage.temporalstart=2012; end=2017-04-27; scheme=W3C-DTFen
Data Creatordc.creatorAgan, Maria Leonora Fatimah
Data Creatordc.creatorValdés Hernández, Maria del C
Date Accessioneddc.date.accessioned2017-05-04T13:28:49Z
Date Availabledc.date.available2017-05-04T13:28:49Z
Citationdc.identifier.citationAgan, Maria Leonora Fatimah; Valdés Hernández, Maria del C. (2017). Manual segmentations of white matter hyperintensities from a subset of 7 ADNI subjects scanned three consecutive years, for inter-/intra-observer reliability analyses, 2012-2017 [dataset]. University of Edinburgh. Centre for Clinical Brain Sciences. https://doi.org/10.7488/ds/2041.en
Persistent Identifierdc.identifier.urihttps://hdl.handle.net/10283/2706
Persistent Identifierdc.identifier.urihttps://doi.org/10.7488/ds/2041
Dataset Description (abstract)dc.description.abstractThis dataset contains structural magnetic resonance imaging (MRI)-derived data from 7 randomly selected participants enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. These data are binary masks of white matter hyperintensities (WMH), all obtained from 21 MRI scans (acquired at three consecutive study visits spaced 12 months apart). These masks were generated using semi-automatic segmentation (i.e., combined thresholding and manual tracing or manual editing of masks) on Mango Version 4.0, available from http://ric.uthscsa.edu/mango/ .en_UK
Dataset Description (TOC)dc.description.tableofcontents* 42 compressed binary image files in nifti-gzip format (i.e. extension .nii.gz) of white matter hyperintensities segmentations. * Document describing the dataset in .docx and .pdf formats.en_UK
Languagedc.language.isoengen_UK
Publisherdc.publisherUniversity of Edinburgh. Centre for Clinical Brain Sciencesen_UK
Relation (Is Referenced By)dc.relation.isreferencedbyhttps://doi.org/10.1016/j.compmedimag.2019.101685en_UK
Relation (Is Referenced By)dc.relation.isreferencedbyLimited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images. MF Rachmadi, MC Valdés-Hernández, H Li, R Guerrero, R Meijboom, et al. Computerized Medical Imaging and Graphics 79, 101685
Relation (Is Referenced By)dc.relation.isreferencedbyEvaluation of four supervised learning schemes in white matter hyperintensities segmentation in absence or mild presence of vascular pathology. Muhammad Febrian Rachmadi, Maria del C Valdés-Hernández, Maria Leonora Fatimah Agan, Taku Komura, Alzheimer’s Disease Neuroimaging Initiative Medical Image Understanding and Analysis 2017, 482-493, Springer, Cham
Relation (Is Referenced By)dc.relation.isreferencedbyVoxel-based irregularity age map (iam) for brain's white matter hyperintensities in mri MF Rachmadi, MC Valdés-Hernández, T Komura 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 321-326, IEEE
Relation (Is Referenced By)dc.relation.isreferencedbySegmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology MF Rachmadi, MC Valdes-Hernandez, MLF Agan, C Di Perri, T Komura, ... Computerized Medical Imaging and Graphics 66, 28-43
Relation (Is Referenced By)dc.relation.isreferencedbyDeep learning vs. conventional machine learning: Pilot study of wmh segmentation in brain mri with absence or mild vascular pathology MF Rachmadi, MC Valdés-Hernández, MLF Agan, T Komura Journal of Imaging 3 (4), 66
Relation (Is Referenced By)dc.relation.isreferencedbyTransfer learning for task adaptation of brain lesion assessment and prediction of brain abnormalities progression/regression using irregularity age map in brain mri MF Rachmadi, MC Valdés-Hernández, T Komura International Workshop on PRedictive Intelligence In Medicine 2018, 85-93, Springer Cham
Relation (Is Referenced By)dc.relation.isreferencedbyAutomatic irregular texture detection in brain mri without human supervision MF Rachmadi, MC Valdés-Hernández, T Komura International Conference on Medical Image Computing and Computer-Assisted Intervention 2018, 506-513, Springer, Cham
Rightsdc.rightsCreative Commons Attribution 4.0 International Public Licenseen
Subjectdc.subjectMRIen_UK
Subjectdc.subjectbrainen_UK
Subjectdc.subjectwhite matter hyperintensitiesen_UK
Subjectdc.subjectsegmentationen_UK
Subjectdc.subjectinter-/intra-observer reliabilityen_UK
Subject Classificationdc.subject.classificationSubjects allied to Medicine::Neuroscienceen_UK
Titledc.titleManual segmentations of white matter hyperintensities from a subset of 7 ADNI subjects scanned three consecutive years, for inter-/intra-observer reliability analysesen_UK
Typedc.typedataseten_UK

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