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 stroke recovery. However, analyzing large datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation, 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 229 T1-weighted MRI scans (n=220) 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 R1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

The dataset includes:

Data Release Download

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bioRxiv Preprint

Click here to access the bioRxiv preprint for ATLAS R1.1.

doi: https://doi.org/10.1101/179614


Sook-Lei Liew1†*, Julia M. Anglin1*, Nick W. Banks1, Matt Sondag1, Kaori L. Ito1, Hosung Kim1, Jennifer Chan1, Joyce Ito1, Connie Jung1, Stephanie Lefebvre1, William Nakamura1, David Saldana1, Allie Schmiesing1, Cathy Tran1, Danny Vo1, Tyler Ard1, Panthea Heydari1, Bokkyu Kim1, Lisa Aziz-Zadeh1, Steven C. Cramer2, Jingchun Liu3, Surjo Soekadar4, Jan-Egil Nordvik5, Lars T. Westlye6,7, Junping Wang3, Carolee Winstein1, Chunshui Yu3, Lei Ai8, Bonhwang Koo8, R. Cameron Craddock8,9, Michael Milham8,9, Matthew Lakich10, Amy Pienta11, Alison Stroud11

Corresponding author: Sook-Lei Liew (sliew @ usc.edu)

*Denotes equal contributions


  1. University of Southern California, Los Angeles, California, USA
  2. University of California, Irvine, Irvine, California, USA
  3. Tianjin Medical University General Hospital, Tianjin, China
  4. University of Tübingen, Tübingen, Germany
  5. Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
  6. NORMENT and KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
  7. Department of Psychology, University of Oslo, Oslo, Norway
  8. Child Mind Institute, New York, New York, USA
  9. Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, USA
  10. University of Texas Medical Branch, Galveston, Texas, USA
  11. University of Michigan, Ann Arbor, Michigan, USA


We would like to acknowledge the following people for their assistance on this effort: Anthony Benitez, Xiaoyu Chen, Cristi Magracia, Ryan Mori, Dhanashree Potdar, Sandyha Prathap.

The archiving of this dataset was specifically supported by the NIH-funded Center for Large Data Research and Data Sharing in Rehabilitation (CLDR) under a Category 2 Pilot Grant (P2CHD06570) and this work was also funded by an NIH K01 award (1K01HD091283).