%0 Journal Article %A Sook-Lei Liew %A Julia M. Anglin %A Nick W. Banks %A Matt Sondag %A Kaori L. Ito %A Hosung Kim %A Jennifer Chan %A Joyce Ito %A Connie Jung %A Nima Khoshab %A Stephanie Lefebvre %A William Nakamura %A David Saldana %A Allie Schmiesing %A Cathy Tran %A Danny Vo %A Tyler Ard %A Panthea Heydari %A Bokkyu Kim %A Lisa Aziz-Zadeh %A Steven C. Cramer %A Jingchun Liu %A Surjo Soekadar %A Jan-Egil Nordvik %A Lars T. Westlye %A Junping Wang %A Carolee Winstein %A Chunshui Yu %A Lei Ai %A Bonhwang Koo %A R. Cameron Craddock %A Michael Milham %A Matthew Lakich %A Amy Pienta %A Alison Stroud %T A large, open source dataset of stroke anatomical brain images and manual lesion segmentations %D 2017 %R 10.1101/179614 %J bioRxiv %P 179614 %X 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. %U https://www.biorxiv.org/content/biorxiv/early/2017/11/08/179614.full.pdf