Abstract
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.
| Language | English (US) |
|---|---|
| Article number | 180011 |
| Journal | Scientific Data |
| Volume | 5 |
| DOIs | |
| State | Published - Feb 20 2018 |
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ASJC Scopus subject areas
- Statistics and Probability
- Information Systems
- Education
- Computer Science Applications
- Statistics, Probability and Uncertainty
- Library and Information Sciences
Cite this
A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. / 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, Richard; Milham, Michael; Lakich, Matthew; Pienta, Amy; Stroud, Alison.
In: Scientific Data, Vol. 5, 180011, 20.02.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A large, open source dataset of stroke anatomical brain images and manual lesion segmentations
AU - Liew, Sook Lei
AU - Anglin, Julia M.
AU - Banks, Nick W.
AU - Sondag, Matt
AU - Ito, Kaori L.
AU - Kim, Hosung
AU - Chan, Jennifer
AU - Ito, Joyce
AU - Jung, Connie
AU - Khoshab, Nima
AU - Lefebvre, Stephanie
AU - Nakamura, William
AU - Saldana, David
AU - Schmiesing, Allie
AU - Tran, Cathy
AU - Vo, Danny
AU - Ard, Tyler
AU - Heydari, Panthea
AU - Kim, Bokkyu
AU - Aziz-Zadeh, Lisa
AU - Cramer, Steven C.
AU - Liu, Jingchun
AU - Soekadar, Surjo
AU - Nordvik, Jan Egil
AU - Westlye, Lars T.
AU - Wang, Junping
AU - Winstein, Carolee
AU - Yu, Chunshui
AU - Ai, Lei
AU - Koo, Bonhwang
AU - Craddock, Richard
AU - Milham, Michael
AU - Lakich, Matthew
AU - Pienta, Amy
AU - Stroud, Alison
PY - 2018/2/20
Y1 - 2018/2/20
N2 - 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.
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 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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U2 - 10.1038/sdata.2018.11
DO - 10.1038/sdata.2018.11
M3 - Article
VL - 5
JO - Scientific data
T2 - Scientific data
JF - Scientific data
SN - 2052-4463
M1 - 180011
ER -