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

Contributed by sliew

Sook-Lei Liew, Julia M. Anglin, Nick W. Banks, Matt Sondag, Kaori L. Ito, Hosung Kim, Jennifer Chan, Joyce Ito, Connie Jung, Nima Khoshab, Stephanie Lefebvre, William Nakamura, David Saldana, Allie Schmiesing, Cathy Tran, Danny Vo, Tyler Ard, Panthea Heydari, Bokkyu Kim, Lisa Aziz-Zadeh, Steven C. Cramer, Jingchun Liu, Surjo Soekadar, Jan-Egil Nordvik, Lars T. Westlye, Junping Wang, Carolee Winstein, Chunshui Yu, Lei Ai, Bonhwang Koo, R. Cameron Craddock, Michael Milham, Matthew Lakich, Amy Pienta and Alison Stroud
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AuthorsSook-Lei Liew, Julia M. Anglin, Nick W. Banks, Matt Sondag, Kaori L. Ito, Hosung Kim, Jennifer Chan, Joyce Ito, Connie Jung, Nima Khoshab, Stephanie Lefebvre, William Nakamura, David Saldana, Allie Schmiesing, Cathy Tran, Danny Vo, Tyler Ard, Panthea Heydari, Bokkyu Kim, Lisa Aziz-Zadeh, Steven C. Cramer, Jingchun Liu, Surjo Soekadar, Jan-Egil Nordvik, Lars T. Westlye, Junping Wang, Carolee Winstein, Chunshui Yu, Lei Ai, Bonhwang Koo, R. Cameron Craddock, Michael Milham, Matthew Lakich, Amy Pienta and Alison Stroud
DescriptionStroke 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.
JournalScientific Data
Contributors
DOI10.1038/sdata.2018.11
Field StrengthNone
id3073
Add DateOct. 12, 2017, 1:32 a.m.