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A large, open source dataset of stroke anatomical brain images and manual lesion segmentations.

  • Author(s): Liew, Sook-Lei
  • Anglin, Julia M
  • Banks, Nick W
  • Sondag, Matt
  • Ito, Kaori L
  • Kim, Hosung
  • Chan, Jennifer
  • Ito, Joyce
  • Jung, Connie
  • Khoshab, Nima
  • Lefebvre, Stephanie
  • Nakamura, William
  • Saldana, David
  • Schmiesing, Allie
  • Tran, Cathy
  • Vo, Danny
  • Ard, Tyler
  • Heydari, Panthea
  • Kim, Bokkyu
  • Aziz-Zadeh, Lisa
  • Cramer, Steven C
  • Liu, Jingchun
  • Soekadar, Surjo
  • Nordvik, Jan-Egil
  • Westlye, Lars T
  • Wang, Junping
  • Winstein, Carolee
  • Yu, Chunshui
  • Ai, Lei
  • Koo, Bonhwang
  • Craddock, R Cameron
  • Milham, Michael
  • Lakich, Matthew
  • Pienta, Amy
  • Stroud, Alison
  • et al.

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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