Dataset Information

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

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.

SUBMITTER: Liew SL

PROVIDER: S-EPMC5819480 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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

Liew Sook-Lei SL Anglin Julia M JM Banks Nick W NW Sondag Matt M Ito Kaori L KL Kim Hosung H Chan Jennifer J Ito Joyce J Jung Connie C Khoshab Nima N Lefebvre Stephanie S Nakamura William W Saldana David D Schmiesing Allie A Tran Cathy C Vo Danny D Ard Tyler T Heydari Panthea P Kim Bokkyu B Aziz-Zadeh Lisa L Cramer Steven C SC Liu Jingchun J Soekadar Surjo S Nordvik Jan-Egil JE Westlye Lars T LT Wang Junping J Winstein Carolee C Yu Chunshui C Ai Lei L Koo Bonhwang B Craddock R Cameron RC Milham Michael M Lakich Matthew M Pienta Amy A Stroud Alison A

Scientific data 20180220

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 les ...[more]

PMID: 29461514

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Project description:PurposePrecise and reproducible hippocampus outlining is important to quantify hippocampal atrophy caused by neurodegenerative diseases and to spare the hippocampus in whole brain radiation therapy when performing prophylactic cranial irradiation or treating brain metastases. This study aimed to quantify systematic differences between methods by comparing regional volume and outline reproducibility of manual, FSL-FIRST and FreeSurfer hippocampus segmentations.Materials and methodsThis study used a dataset from ADNI (Alzheimer's Disease Neuroimaging Initiative), including 20 healthy controls, 40 patients with mild cognitive impairment (MCI), and 20 patients with Alzheimer's disease (AD). For each subject back-to-back (BTB) T1-weighted 3D MPRAGE images were acquired at time-point baseline (BL) and 12 months later (M12). Hippocampi segmentations of all methods were converted into triangulated meshes, regional volumes were extracted and regional Jaccard indices were computed between the hippocampi meshes of paired BTB scans to evaluate reproducibility. Regional volumes and Jaccard indices were modelled as a function of group (G), method (M), hemisphere (H), time-point (T), region (R) and interactions.ResultsFor the volume data the model selection procedure yielded the following significant main effects G, M, H, T and R and interaction effects G-R and M-R. The same model was found for the BTB scans. For all methods volumes reduces with the severity of disease. Significant fixed effects for the regional Jaccard index data were M, R and the interaction M-R. For all methods the middle region was most reproducible, independent of diagnostic group. FSL-FIRST was most and FreeSurfer least reproducible.Discussion/conclusionA novel method to perform detailed analysis of subtle differences in hippocampus segmentation is proposed. The method showed that hippocampal segmentation reproducibility was best for FSL-FIRST and worst for Freesurfer. We also found systematic regional differences in hippocampal segmentation between different methods reinforcing the need of adopting harmonized protocols.

Dataset Information

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

Publications

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

Similar Datasets

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