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The Anatomical Tracings of Lesions after Stroke (ATLAS) Dataset - Release 1.2, 2018

Version
v3
Resource Type
Dataset : clinical data, images: photographs, drawings, graphical representations
Creator
  • Liew, Sook-Lei
Other Title
  • ATLAS (Alternative Title)
  • Version 3 (Subtitle)
Publication Date
2017-08-24
Publication Place
Ann Arbor, Michigan
Publisher
  • Inter-University Consortium for Political and Social Research
Funding Reference
  • Center for Large Data Research and Data Sharing in Rehabilitation
Language
English
Free Keywords
Schema: ICPSR
automated lesion segmentation; cerebrovascular accident; diagnostic imaging; images; lesion segmentation; Magnetic Resonance Imaging; MRI; stroke; stroke rehabilitation
Description
  • Abstract

    To access this data collection, please click on the Restricted Data button above. You will need to download and complete the data use agreement and then email it to icpsr-addep@umich.edu. The instructions are in the form. The Anatomical Tracings of Lesions After Stroke (ATLAS) Dataset - Release 1.2 is an open-source data collection consisting a total of 304 T1-weighted MRIs (Magnetic Resonance Imaging) with manually segmented diverse lesions and metadata. The goal of ATLAS is to provide the research community with a standardized training and testing dataset for lesion segmentation algorithms on T1-weighted MRIs. From 11 cohorts worldwide, 304 MRI images were collected from research groups in the ENIGMA Stroke Recovery Working Group consortium. Images consisted of T1-weighted anatomical MRIs of individuals after stroke. For each MRI, brain lesions were identified and masks were manually drawn on each individual brain in native space using MRIcron, an open-source tool for brain imaging visualization and defining volumes of interest. A minimum of one lesion mask was identified for each individual MRI. If additional, separate lesions were identified, they were traced as separate masks. A separate tracer performed quality control on each lesion mask. This included assessing the accuracy of the lesion segmentations, revising the lesion mask if needed, and categorizing the lesions to generate additional data such as lesion location. In addition, an expert neuroradiologist reviewed all lesions to provide additional qualitative descriptions of the type of stroke, vascular territory, and intensity of white matter disease. This dataset is provided in both native subject space and normalized to a standard template (the MNI-152 template).
  • Methods

    Research groups in the ENIGMA Stroke Recovery Working Group consortium collected 304 MRI images from 11 cohorts worldwide. Images consisted of T1-weighted anatomical MRIs of individuals after stroke. For each MRI, brain lesions were identified and masks were manually drawn on each individual brain in native space using MRIcron, an open-source tool for brain imaging visualization and defining volumes of interest. A minimum of one lesion mask was identified for each individual MRI. If additional, separate lesions were identified, they were traced as separate masks. A separate tracer performed quality control on each lesion mask. This included assessing the accuracy of the lesion segmentations, revising the lesion mask if needed, and categorizing the lesions to generate additional data such as lesion location. In addition, an expert neuroradiologist reviewed all lesions to provide additional qualitative descriptions of the type of stroke, vascular territory, and intensity of white matter disease. The data collection is provided in both native subject space and normalized to a standard template (the MNI-152 template).
  • Methods

    ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Performed recodes and/or calculated derived variables..
  • Abstract

    Datasets:

    • DS1: Dataset
Temporal Coverage
  • Time period: 2018
Geographic Coverage
  • Global
Sampled Universe
MRIs of individuals from around the world who have suffered a stroke. Smallest Geographic Unit: None
Sampling
Convenience samples at 11 different locations worldwide.
Collection Mode
  • mixed mode
Note
2018-11-27 This study was updated with corrections to images in Cohorts 1 and 2 in the previous dataset. The study also included a new version of the metadata data file. The ICPSR codebook has also been updated based on that file.2017-12-22 This study was updated in order to include a new version of the metadata data files and a supplementary information scanner header attributes document. The ICPSR codebook has therefore also been updated. Funding institution(s): Center for Large Data Research and Data Sharing in Rehabilitation (P2CHD06570).
Availability
Delivery
One or more files in this study are not available for download due to special restrictions; consult the study documentation to learn more on how to obtain the data.
Alternative Identifiers
  • 36684 (Type: ICPSR Study Number)
Relations
  • Is new version of
    DOI: 10.3886/ICPSR36684.v2
Publications
  • Liew, Sook-Lei, Anglin, Julia M., Banks, Nick W., Sondag, Matt, Ito, Kaori L., Kim, Hosung, Chan, Jennifer, Ito, Joyce, Jung, Connie, Khoshab, Nima, Lefebvre, Stephanie, Nakamura, William, Saldana, David, Schmiesing, Allie, Tran, Cathy, Vo, Danny, Ard, Tyler, Heydari, Panthea, Kim, Bokkyu, Aziz-Zadeh, Lisa, Cramer, Steven C., Liu, Jingchun, Soekadar, Surjo, Nordvik, Jan-Egil, Westlye, Lars T., Wang, Junping, Winstein, Carolee, Yu, Chunshui, Ai, Lei, Koo, Bonhwang, Craddock, R. Cameron, Milham, Michael, Lakich, Matthew, Pienta, Amy, Stroud, Alison. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific Data.5, 1800112018.
    • ID: 10.1038/sdata.2018.11 (DOI)
  • Liew, Sook-Lei, Anglin, Julia M., Banks, Nick W., Sondag, Matt, Ito, Kaori L., Kim, Hosung, Chan, Jennifer, Ito, Joyce, Jung, Connie, Lefebvre, Stephanie, Nakamura, William, Saldana, David, Schmiesing, Allie, Tran, Cathy, Vo, Danny, Ard, Tyler, Heydari, Panthea, Kim, Bokkyu, Aziz-Zadeh, Lisa, Cramer, Steven C., Liu, Jingchun, Soekadar, Surjo, Nordvik, Jan-Egil, Westlye, Lars T., Wang, Junping, Winstein, Carolee, Yu, Chunshui, Ai, Lei, Koo, Bonhwang, Craddock, R. Cameron, Miham, Michael, Lakich, Matthew, Pienta, Amy, Stroud, Allison. The Anatomical Tracings of Lesions After Stroke (ATLAS) Dataset - Release 1.1. bioRxiv.Unrefereed preprint. 2017.
    • ID: 10.1101/179614 (DOI)

Update Metadata: 2018-11-27 | Issue Number: 2 | Registration Date: 2018-11-27

Liew, Sook-Lei (2017): The Anatomical Tracings of Lesions after Stroke (ATLAS) Dataset - Release 1.2, 2018. Version 3. Version: v3. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/ICPSR36684.v3