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  • 标题:A large, open source dataset of stroke anatomical brain images and manual lesion segmentations
  • 本地全文:下载
  • 作者:Sook-Lei Liew ; Julia M. Anglin ; Nick W. Banks
  • 期刊名称:Scientific Data
  • 电子版ISSN:2052-4463
  • 出版年度:2018
  • 卷号:5
  • DOI:10.1038/sdata.2018.11
  • 语种:English
  • 出版社:Nature Publishing Group
  • 摘要: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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