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The Anatomical Tracings of Lesions after Stroke (ATLAS) Dataset - Release 1.1, 2017 (ICPSR 36684)

Alternate Title:   ATLAS

Principal Investigator(s): Liew, Sook-Lei, USC Chan Division of Occupational Science & Occupational Therapy, University of Southern California

Summary:

The Anatomical Tracings of Lesions After Stroke (ATLAS) Dataset - Release 1.1 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. This data collection can also be used to compare the performance of different lesion segmentation techniques.

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).

Access Notes

  • One or more files in this data collection have special restrictions ; consult the restrictions note to learn more. Additional information can also be found in the Use Agreement.

    This data collection may not be used for any purpose other than statistical reporting and analysis. Use of these data to learn the identity of any person or establishment is prohibited. To obtain the restricted data, users must complete the Restricted Data Use Agreement available for download from the Dataset(s) section of this study home page. Users must agree to the terms and conditions of the agreement and send the form to ADDEP staff.

    Any public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.

Dataset(s)

Dataset
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Study Description

Citation

Liew, Sook-Lei. The Anatomical Tracings of Lesions after Stroke (ATLAS) Dataset - Release 1.1, 2017. ICPSR36684-v1. [distributor], 2017-08-24. https://doi.org/10.3886/ICPSR36684.v1

Persistent URL: https://doi.org/10.3886/ICPSR36684.v1

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Funding

This study was funded by:

  • Center for Large Data Research and Data Sharing in Rehabilitation (P2CHD06570)

Scope of Study

Subject Terms:    automated lesion segmentation, images, lesion segmentation, MRI, rehabilitation, stroke

Smallest Geographic Unit:    None

Geographic Coverage:    Global

Time Period:   

  • 2017

Unit of Observation:    Individual

Universe:    MRIs of individuals from around the world that have suffered a stroke.

Data Type(s):    clinical data, images: photographs, drawings, graphical representations

Data Collection Notes:

Before analyzing the data, users are encouraged to review the README file and the PI Documentation file, both available for download from the study home page. Please contact ADDEP staff at icpsr-addep@umich.edu for further information about the data.

Users are also encouraged to refer to the bioRxiv article for any additional information about the study.

Lesions were segmented using the NITRC open source software MRIcron which can be downloaded from the NITRC website. Users can also quickly and easily view the brains on BrainBox, an open-source Web application to collaboratively annotate and segment neuroimaging data available online. For additional quick quantification, a small toolbox was created. It is called SRQL (Semi-automated Robust Quantification of Lesions), which includes three features: it uses a semi-automated white matter intensity correction, outputs a report of descriptive statistics on lesions, and gives users the option to perform analyses in native or standard space. Finally, any issues or feedback can be submitted on the ATLAS GitHub page, on which any updates, software, and additional releases will also be announced.

Methodology

Study Design:    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).

Sample:    Convenience samples at 11 different locations worldwide

Time Method:    Cross-sectional

Weight:    None

Mode of Data Collection:    mixed mode

Extent of Processing:   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.

Version(s)

Original ICPSR Release:   2017-08-24

Related Publications

    2017
    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.
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