DOI: 10.3886/icpsr36684.v2
title: The Anatomical Tracings of Lesions after Stroke (ATLAS) Dataset - Release 1.1, 2017
title: Version 2

description: <strong> 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. </strong> 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. 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).
description: 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).
description: None
description: Convenience samples at 11 different locations worldwide.
description: Datasets: DS1: The Anatomical Tracings of Lesions after Stroke (ATLAS) Dataset - Release 1.1, 2017
description: MRIs of individuals from around the world who have suffered a stroke. Smallest Geographic Unit: None
description: mixed mode 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.