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

Name: The Anatomical Tracings of Lesions after Stroke (ATLAS) Dataset - Release 1.1, 2017
Published: 2017-08-24
License: https://www.icpsr.umich.edu/web/ICPSR/studies/36684/terms

Version Date: Aug 24, 2017 View help for published

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

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

Version V1 (see more versions)

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V4 [2021-12-01] unpublished
V3 [2018-11-27] unpublished
V2 [2017-12-22] unpublished
V1 [2017-08-25] unpublished

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Additional information about this collection can be found in Version History.

2017-08-24 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.

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Alternate Title View help for Alternate Title

ATLAS

Summary View help for 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).

Citation View help for Citation

Liew, Sook-Lei. The Anatomical Tracings of Lesions after Stroke (ATLAS) Dataset - Release 1.1, 2017. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2017-08-24. https://doi.org/10.3886/ICPSR36684.v1

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Funding View help for Funding

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

Subject Terms View help for Subject Terms

automated lesion segmentation images lesion segmentation MRI rehabilitation stroke

Geographic Coverage View help for Geographic Coverage

Global

Smallest Geographic Unit View help for Smallest Geographic Unit

None

Restrictions View help for 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.

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Time Period(s) View help for Time Period(s)

2017

Data Collection Notes View help for Data Collection Notes

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.
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.

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Study Design View help for 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).