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ZENODO
Dataset . 2020
Data sources: Datacite
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Dataset . 2020
Data sources: ZENODO
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2020
Data sources: Datacite
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Duke Liver Dataset (MRI) v2

Authors: Macdonald, Jacob A.; Zhu, Zhe; Konkel, Brandon; Mazurowski, Maciej; Wiggins, Walter; Bashir, Mustafa;

Duke Liver Dataset (MRI) v2

Abstract

The Duke Liver DataSet (DLDS) provides over 2000 anonymized MRI image series acquired in routine liver MRI protocols across 105 subjects that can be used to train algorithms for two applications: (1) Series identification (2) Liver segmentation The series identification grouping (Series_Classification.zip) includes 2146 unique image series across all 105 subjects. The image series encompass seventeen different contrast types including different pulse sequences, contrast bolus phases, and imaging orientations. A randomized, alphabetical series label corresponding to one of these seventeen contrast types is assigned to each contrast type. The file SeriesClassificationKey.csv includes a list of all image series included in the dataset and their corresponding label. The file SequenceTypes.csv shows the mapping of these labels to contrast type. It is intended that these labels can be used to train an automated series classification model for abdominal MRI. The liver segmentation sub-grouping (Segmentation.zip) includes 310 unique image series across a subset of 95 subjects. The image series encompass four main contrast types: axial in-phase, axial opposed, axial precontrast fat-suppressed T1w, and contrast-enhanced portal venous t1w. It is intended that this data can be used to train an automated liver segmentation model. The file SegmentationKey.csv includes a list of the cases with included masks files. Since there is overlap in these series with the series identification dataset, corresponding series labels are also included. In both datasets, cases are organized in directories by DLDS ID -> Series number. The segmentation cases are further divided into image and mask sub-directories. Please note that a previous version of this dataset (version 1.0.0) withheld roughly 25% of series labels and segmentations. These have been restored to the dataset for completeness. As reference for users wishing to know which data was added (or looking for a list of subjects to remove for a convenient test set), labels and masks were previously withheld for the following DLDS id's: [0002, 0003, 0004, 0017, 0020, 0023, 0031, 0032, 0033, 0035, 0038, 0042, 0046, 0050, 0063, 0074, 0075, 0078, 0083, 0084, 0096, 0097, 0098, 0100, 0103, 0104].

Related Organizations
Keywords

Segmentation, Liver, Contrast, Classification, Series, MRI

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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