A large, open source dataset of stroke anatomical brain images and manual lesion segmentations
dc.date.accessioned | 2019-08-13T05:26:08Z | |
dc.date.available | 2019-08-13T05:26:08Z | |
dc.date.created | 2018-07-12T10:18:13Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | 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 Saldaña, 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 Tjelta Wang, Junping Winstein, Carolee Yu, Chunshui Ai, Lei Koo, Bonhwang Craddock, R. Cameron Milham, Michael Peter Lakich, Matthew Pienta, Amy Stroud, Alison . A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific Data. 2018, 5 | |
dc.identifier.uri | http://hdl.handle.net/10852/69107 | |
dc.description.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. | |
dc.language | EN | |
dc.publisher | Nature Publishing Group | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | A large, open source dataset of stroke anatomical brain images and manual lesion segmentations | |
dc.type | Journal article | |
dc.creator.author | Liew, Sook-Lei | |
dc.creator.author | Anglin, Julia M. | |
dc.creator.author | Banks, Nick W. | |
dc.creator.author | Sondag, Matt | |
dc.creator.author | Ito, Kaori L. | |
dc.creator.author | Kim, Hosung | |
dc.creator.author | Chan, Jennifer | |
dc.creator.author | Ito, Joyce | |
dc.creator.author | Jung, Connie | |
dc.creator.author | Khoshab, Nima | |
dc.creator.author | Lefebvre, Stephanie | |
dc.creator.author | Nakamura, William | |
dc.creator.author | Saldaña, David | |
dc.creator.author | Schmiesing, Allie | |
dc.creator.author | Tran, Cathy | |
dc.creator.author | Vo, Danny | |
dc.creator.author | Ard, Tyler | |
dc.creator.author | Heydari, Panthea | |
dc.creator.author | Kim, Bokkyu | |
dc.creator.author | Aziz-Zadeh, Lisa | |
dc.creator.author | Cramer, Steven C. | |
dc.creator.author | Liu, Jingchun | |
dc.creator.author | Soekadar, Surjo | |
dc.creator.author | Nordvik, Jan Egil | |
dc.creator.author | Westlye, Lars Tjelta | |
dc.creator.author | Wang, Junping | |
dc.creator.author | Winstein, Carolee | |
dc.creator.author | Yu, Chunshui | |
dc.creator.author | Ai, Lei | |
dc.creator.author | Koo, Bonhwang | |
dc.creator.author | Craddock, R. Cameron | |
dc.creator.author | Milham, Michael Peter | |
dc.creator.author | Lakich, Matthew | |
dc.creator.author | Pienta, Amy | |
dc.creator.author | Stroud, Alison | |
cristin.unitcode | 185,17,5,0 | |
cristin.unitname | Psykologisk institutt | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.cristin | 1596841 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Scientific Data&rft.volume=5&rft.spage=&rft.date=2018 | |
dc.identifier.jtitle | Scientific Data | |
dc.identifier.volume | 5 | |
dc.identifier.doi | http://dx.doi.org/10.1038/sdata.2018.11 | |
dc.identifier.urn | URN:NBN:no-72259 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 2052-4463 | |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/69107/1/sdata201811.pdf | |
dc.type.version | PublishedVersion | |
dc.relation.project | NFR/249795 |
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