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

Sook Lei Liew, Julia M. Anglin, Nick W. Banks, Matt Sondag, Kaori L. Ito, Hosung Kim, Jennifer Chan, Joyce Ito, Connie Jung, Nima Khoshab, Stephanie Lefebvre, William Nakamura, David Saldana, Allie Schmiesing, Cathy Tran, Danny Vo, Tyler Ard, Panthea Heydari, Bokkyu Kim, Lisa Aziz-Zadeh & 15 others Steven C. Cramer, Jingchun Liu, Surjo Soekadar, Jan Egil Nordvik, Lars T. Westlye, Junping Wang, Carolee Winstein, Chunshui Yu, Lei Ai, Bonhwang Koo, Richard Craddock, Michael Milham, Matthew Lakich, Amy Pienta, Alison Stroud

Research output: Contribution to journalArticle

  • 2 Citations

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.

LanguageEnglish (US)
Article number180011
JournalScientific Data
Volume5
DOIs
StatePublished - Feb 20 2018

Fingerprint

stroke
Stroke
Open Source
Brain
brain
Segmentation
Patient rehabilitation
Magnetic resonance imaging
Neuroimaging
Rehabilitation
Disability
Tracing
Biomarkers
rehabilitation
Metadata
disability
Learning systems
Personnel
gold standard
Recovery

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

Cite this

Liew, S. L., Anglin, J. M., Banks, N. W., Sondag, M., Ito, K. L., Kim, H., ... Stroud, A. (2018). A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific Data, 5, [180011]. https://doi.org/10.1038/sdata.2018.11

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 journalArticle

Liew, SL, Anglin, JM, Banks, NW, Sondag, M, Ito, KL, Kim, H, Chan, J, Ito, J, Jung, C, Khoshab, N, Lefebvre, S, Nakamura, W, Saldana, D, Schmiesing, A, Tran, C, Vo, D, Ard, T, Heydari, P, Kim, B, Aziz-Zadeh, L, Cramer, SC, Liu, J, Soekadar, S, Nordvik, JE, Westlye, LT, Wang, J, Winstein, C, Yu, C, Ai, L, Koo, B, Craddock, R, Milham, M, Lakich, M, Pienta, A & Stroud, A 2018, 'A large, open source dataset of stroke anatomical brain images and manual lesion segmentations' Scientific Data, vol. 5, 180011. https://doi.org/10.1038/sdata.2018.11
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. / A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. In: Scientific Data. 2018 ; Vol. 5.
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