The Anatomical Tracings of Lesions after Stroke (ATLAS) Dataset - Release 2.0, 2021 (ICPSR 36684)

Version Date: Aug 8, 2022

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

https://doi.org/10.3886/ICPSR36684.v5

Version V5 ()

  • V5 [2022-08-08]
  • V4 [2021-12-01] unpublished
  • V3 [2018-11-27] unpublished
  • V2 [2017-12-22] unpublished
  • V1 [2017-08-25] unpublished
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Alternate Title

ATLAS

Summary

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 2.0 is an open-source data collection consisting a total of 955 T1-weighted MRIs (Magnetic Resonance Imaging) with manually segmented diverse lesions and metadata. ATLAS v2.0 has been split into a public release of 655 T1w MRIs and lesion masks and a hidden test dataset of 300 T1w MRIs. For the hidden dataset, only the T1s MRIs are available. The accompanying manually segmented lesion masks will be made available only for testing algorithm performance in lesion segmentation challenges and competitions. 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 33 cohorts worldwide, 955 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 ITK-SNAP (version 3.8.0). After tracing, researchers reviewed and edited lesion masks as necessary using a standardized quality control protocol. In a subset of the data, lesion masks were received from the originating site and edited and checked for quality by the team. All team members received lesion-tracing training and followed a standard operating protocol for tracing lesions to ensure inter-rater reliability on all manually traced masks. All lesion masks were checked twice for quality by trained team members. During the quality control process, researchers ensured that the boundaries of the lesion segmentation were accurate and that all identifiable lesions in the brain were traced. All subject files have undergone a lesion tracing and preprocessing pipeline and are named and stored in accordance with the Brain Imaging Data Structure (BIDS) This dataset is provided in both native subject space and normalized to a standard template (the MNI-152 template).

Citation

Liew, Sook-Lei. The Anatomical Tracings of Lesions after Stroke (ATLAS) Dataset - Release 2.0, 2021. Inter-university Consortium for Political and Social Research [distributor], 2022-08-08. https://doi.org/10.3886/ICPSR36684.v5

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Funding

Center for Large Data Research and Data Sharing in Rehabilitation (P2CHD06570), United States Department of Health and Human Services. National Institutes of Health. National Institute of Neurological Disorders and Stroke (R01NS115845)

Subject Terms

Geographic Coverage

Smallest Geographic Unit

None

Restrictions

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.

Distributor(s)

Inter-university Consortium for Political and Social Research
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Time Period(s)

2018 -- 2021

Data Collection Notes

  1. 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.

  2. In the previous release, ATLAS v1.2, 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 'issues' page, on which any updates, software, and additional releases will also be announced.
  3. The Pipeline for Analyzing Lesions After Stroke (PALS) open-source software has been updated, which allows users to easily calculate lesion volume, evaluate lesion overlap with brain regions of interest, and create lesion overlap images.
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Study Design

Research groups in the ENIGMA Stroke Recovery Working Group consortium collected 955 MRI images from 33 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 ITK-SNAP (version 3.8.0). After tracing, researchers reviewed and edited lesion masks as necessary using a standardized quality control protocol. In a subset of the data, lesion masks were received from the originating site and edited and checked for quality by the team. All team members received lesion-tracing training and followed a standard operating protocol for tracing lesions to ensure inter-rater reliability on all manually traced masks. All lesion masks were checked twice for quality by trained team members. During the quality control process, researchers ensured that the boundaries of the lesion segmentation were accurate and that all identifiable lesions in the brain were traced. The data collection is provided in both native subject space and normalized to a standard template (the MNI-152 template).

Sample

Convenience samples at 33 different locations worldwide. This is a harmonized sample of retrospective MRI data collected from 20 different international research studies.

Time Method

Cross-sectional

Universe

MRIs of individuals from around the world who have suffered a stroke.

Unit(s) of Observation

Individual

Data Type(s)

Mode of Data Collection

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Original Release Date

2017-08-24

Version History

2022-08-08 This study was updated with a new metadata file, containing information for the 655 training cases in the study. A column that describes the time of MRI acquisition relative to stroke onset was added.

2021-12-01 This study was updated to replace all existing data and documentation with new files.

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.

2018-02-15 The citation of this study may have changed due to the new version control system that has been implemented. The previous citation was:
  • Liew, Sook-Lei. The Anatomical Tracings of Lesions after Stroke (ATLAS) Dataset - Release 2.0, 2021. ICPSR36684-v5. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-08-08. http://doi.org/10.3886/ICPSR36684.v5

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.

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Weight

None

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Notes

  • The 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.

  • One or more files in this data collection have special restrictions. Restricted data files are not available for direct download from the website; click on the Restricted Data button to learn more.

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