A large, open source dataset of stroke anatomical brain images and manual lesion segmentations
Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabil...
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| Published in: | Scientific data Vol. 5; no. 1; p. 180011 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
20-02-2018
Nature Publishing Group |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.
Design Type(s)
parallel group design
Measurement Type(s)
nuclear magnetic resonance assay
Technology Type(s)
MRI Scanner
Factor Type(s)
regional part of brain • cerebral hemisphere • Clinical Diagnosis
Sample Characteristic(s)
Homo sapiens • brain
Machine-accessible metadata file describing the reported data
(ISA-Tab format) |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3 NFR/249795 S.-L.L. conceptualized the study, reviewed lesions, analyzed data, established archives, and contributed to the writing and editing of the manuscript. J.M.A. segmented and reviewed lesions, oversaw the organization to the segmentation process and contributed to the writing and editing of the manuscript. N.W.B. organized, segmented and reviewed lesions. M.S. provided the neuroradiology expertise and information. K.L.I. and H.K. performed data analysis. H.K. also performed data processing and generated the standardized dataset and probabilistic lesion maps. T.A. provided data visualization expertise and generated the figures/videos. J.C., D.S., A.S. J.I., C.J., W.N., D.V. and S.L. segmented and/or reviewed lesions. P.H., B.K., N.K., L.A.-Z., S.C.C., J.L., S.S., L.T.W., J.W., C.W., C.Y. collected and provided the MRI data. M.L., A.P., and A.S. handled the archiving of the data. These authors contributed equally to this work. |
| ISSN: | 2052-4463 2052-4463 |
| DOI: | 10.1038/sdata.2018.11 |