PT  - JOURNAL ARTICLE
AU  - Sook-Lei Liew
AU  - Julia M. Anglin
AU  - Nick W. Banks
AU  - Matt Sondag
AU  - Kaori L. Ito
AU  - Hosung Kim
AU  - Jennifer Chan
AU  - Joyce Ito
AU  - Connie Jung
AU  - Stephanie Lefebvre
AU  - William Nakamura
AU  - David Saldana
AU  - Allie Schmiesing
AU  - Cathy Tran
AU  - Danny Vo
AU  - Tyler Ard
AU  - Panthea Heydari
AU  - Bokkyu Kim
AU  - Lisa Aziz-Zadeh
AU  - Steven C. Cramer
AU  - Jingchun Liu
AU  - Surjo Soekadar
AU  - Jan-Egil Nordvik
AU  - Lars T. Westlye
AU  - Junping Wang
AU  - Carolee Winstein
AU  - Chunshui Yu
AU  - Lei Ai
AU  - Bonhwang Koo
AU  - R. Cameron Craddock
AU  - Michael Milham
AU  - Matthew Lakich
AU  - Amy Pienta
AU  - Alison Stroud
TI  - The Anatomical Tracings of Lesions After Stroke (ATLAS) Dataset - Release 1.1
AID  - 10.1101/179614
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 179614
4099  - http://biorxiv.org/content/early/2017/08/26/179614.short
4100  - http://biorxiv.org/content/early/2017/08/26/179614.full
AB  - 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 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 R1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.