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

被引：180

作者：

Liew, Sook-Lei ^{[1
]}

Anglin, Julia M. ^{[1
]}

Banks, Nick W. ^{[1
]}

Sondag, Matt ^{[1
]}

Ito, Kaori L. ^{[1
]}

Kim, Hosung ^{[1
]}

Chan, Jennifer ^{[1
]}

Ito, Joyce ^{[1
]}

Jung, Connie ^{[1
]}

Khoshab, Nima ^{[2
]}

Lefebvre, Stephanie ^{[1
]}

Nakamura, William ^{[1
]}

Saldana, David ^{[1
]}

Schmiesing, Allie ^{[1
]}

Tran, Cathy ^{[1
]}

Vo, Danny ^{[1
]}

Ard, Tyler ^{[1
]}

Heydari, Panthea ^{[1
]}

Kim, Bokkyu ^{[1
]}

Aziz-Zadeh, Lisa ^{[1
]}

Cramer, Steven C. ^{[2
]}

Liu, Jingchun ^{[3
]}

Soekadar, Surjo ^{[4
]}

Nordvik, Jan-Egil ^{[5
]}

Westlye, Lars T. ^{[6
,7
,8
]}

Wang, Junping ^{[3
]}

Winstein, Carolee ^{[1
]}

Yu, Chunshui ^{[3
]}

Ai, Lei ^{[9
]}

Koo, Bonhwang ^{[9
]}

Craddock, R. Cameron ^{[9
,10
]}

Milham, Michael ^{[9
,10
]}

Lakich, Matthew ^{[11
]}

Pienta, Amy ^{[12
]}

Stroud, Alison ^{[12
]}

机构：

[1] Univ Southern Calif, Los Angeles, CA 90089 USA

[2] Univ Calif Irvine, Irvine, CA 92697 USA

[3] Tianjin Med Univ, Gen Hosp, Tianjin 30051, Peoples R China

[4] Univ Tubingen, D-72076 Tubingen, Germany

[5] Sunnaas Rehabil Hosp HT, N-1453 Nesodden, Norway

[6] Oslo Univ Hosp, NORMENT, N-0372 Oslo, Norway

[7] Oslo Univ Hosp, KG Jebsen Ctr Psychosis Res, Div Mental Hlth & Addict, N-0372 Oslo, Norway

[8] Univ Oslo, Dept Psychol, N-0315 Oslo, Norway

[9] Child Mind Inst, New York, NY 10022 USA

[10] Nathan S Kline Inst Psychiat Res, Orangeburg, NY 10962 USA

[11] Univ Texas Med Branch, Galveston, TX 77555 USA

[12] Univ Michigan, Ann Arbor, MI 48104 USA

来源：

SCIENTIFIC DATA | 2018年 / 5卷

关键词：

RECOVERY; PREDICT; DISEASE; GAINS; TIME;

D O I：

10.1038/sdata.2018.11

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

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.

引用

页数：11

共 31 条

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