ArticleScientific data2022
A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 2 of them syntheses that pooled it.
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Who cites it
57 citing papers in PubMed, 2 syntheses or guidelines pooled it, 140 citations in OpenAlex.
- Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.Radiology · 2025Pooled it
- Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.Frontiers in neuroscience · 2024Pooled it
- Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI.Research square · 2026Article
- Bioengineered Materials for Skull Base Reconstruction-Current Clinical Applications: Systematic Review and Meta-analysis.Journal of neurological surgery. Part B, Skull base · 2026Article
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- Adaptive multimodal fusion via Gated Parallel Mamba architecture for ultra-high-precision stroke lesion segmentation in medical imaging.PLOS digital health · 2026Article
- Estimating white matter hyperintensities volume in individuals with stroke using T1-weighted images.Neuroimage. Reports · 2026Article
- Optimizing the Approach to Calculating CST Lesion Load in Ischemic Stroke Patients for Understanding Motor Outcomes.Neurorehabilitation and neural repair · 2026Article
- White Matter Infarct Detection with Transformer and Auto-ML-Derived Models.Brain sciences · 2026Article
- Structural connectome changes as biomarkers of stroke recovery: a longitudinal 7T MRI study.BMC neuroscience · 2026Article
- Ten Recommendations for Improving Research on Stroke Motor Rehabilitation: A Unified Perspective.Restorative neurology and neuroscience · 2026Review
- Benchmarks and methods for 3D medical image retrieval.Scientific reports · 2026Article
- A generalizable foundation model for analysis of human brain MRI.Nature neuroscience · 2026Article
- Associations between contralesional neuroplasticity and motor impairment through deep learning-derived MRI regional brain age in chronic stroke (ENIGMA): a multicohort, retrospective, observational study.The Lancet. Digital health · 2026Observational
- LBMNet: a hybrid multi-scale CNN-Mamba framework for enhanced 3D stroke lesion segmentation in MRI.Frontiers in medicine · 2026Article
- Image-conditioned latent rectified flow models for 3D medical anomaly localisation.Frontiers in radiology · 2026Article
- Plug and Play Labeling Strategies for Boosting Small Brain Lesion Segmentation.Pattern recognition letters · 2026Article
- A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development.Journal of imaging · 2025Review
- Collaborative AI for precision neurorehabilitation: a roadmap.Journal of neuroengineering and rehabilitation · 2025Review
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
63 authors at 20 institutions in 13 countries.
Funding
Abstract
Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.