A large, open source dataset of stroke anatomical brain images and manual lesion segmentations
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Abstract
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.
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- Authors
- Liew, S.-L., Anglin, J. M., Banks, N. W., Sondag, M., Ito, K. L., Kim, H., Chan, J., Ito, J., Jung, C., Khoshab, N., Lefebvre, S., Nakamura, W., Saldana, D., Schmiesing, A., Tran, C., Vo, D., Ard, T., Heydari, P., Kim, B., Aziz-Zadeh, L., Cramer, S. C., Liu, J., Soekadar, S., Nordvik, J.-E., Westlye, L. T., Wang, J., Winstein, C., Yu, C., Ai, L., Koo, B., Craddock, R. C., Milham, M., Lakich, M., Pienta, A., & Stroud, A.
- Affiliations
- University of Southern California (US)
- University of California, Irvine (US)
- Tianjin Medical University General Hospital (CN)
- University of Tübingen (DE)
- Sunnaas sykehus (NO)
- Oslo University Hospital (NO)
- Child Mind Institute (US)
- Nathan Kline Institute for Psychiatric Research (US)
- The University of Texas Medical Branch at Galveston (US)
- University of Michigan (US)
- Corresponding author
- Liew, Sook-Lei
- ORCID
- Venue
- Scientific Data
- Published
- 2018-02-20
- Volume
- 5
- Issue
- 1
- Pages
- 180011-180011
- ISSN
- 2052-4463 (ISSN-L)
- PMC
- PMC5819480
- OpenAlex
- W2791723176
- Cited by
- 283
- References
- 30
- Citations by year
- 2026: 20 · 2025: 35 · 2024: 35 · 2023: 49 · 2022: 42 · 2021: 37 · 2020: 32 · 2019: 25 · 2018: 6 · 2017: 1
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Data (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Funders
- National Institutes of Health (NIH K01, P2CHD06570)
- National Institute of Biomedical Imaging and Bioengineering (P41EB015922)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (K01HD091283, K12HD055929, P2CHD065702)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Epidemiology › Acute Ischemic Stroke Management
- Keywords
- stroke, lesion segmentation, manual segmentation, T1-weighted imaging, stroke rehabilitation, ATLAS dataset, segmentation algorithm evaluation
- MeSH terms
- Adult, Algorithms, Brain, Humans, Magnetic Resonance Imaging, Stroke, Neuroimaging
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