2Stroke is the leading cause of adult disability worldwide, with up to two-thirds 3 of individuals experiencing long-term disabilities. Large-scale neuroimaging 4 studies have shown promise in identifying robust biomarkers (e.g., measures 5 of brain structure) of long-term stroke recovery following rehabilitation. 6However, analyzing large rehabilitation-related datasets is problematic due to 7 barriers in accurate stroke lesion segmentation. Manually-traced lesions are 8 currently the gold standard for lesion segmentation on T1-weighted MRIs, but 9 are labor intensive and require anatomical expertise. While algorithms have 10 been developed to automate this process, the results often lack accuracy. 11Newer algorithms that employ machine-learning techniques are promising, yet 12 these require large training datasets to optimize performance. 1.1 will be a useful resource to assess and improve the accuracy of current 19 lesion segmentation methods.

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.