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

View ORCID ProfileSook-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, Steven C. Cramer, Jingchun Liu, Surjo Soekadar, Jan-Egil Nordvik, Lars T. Westlye, Junping Wang, Carolee Winstein, Chunshui Yu, Lei Ai, Bonhwang Koo, R. Cameron Craddock, Michael Milham, Matthew Lakich, Amy Pienta, Alison Stroud
doi: https://doi.org/10.1101/179614
Sook-Lei Liew
1University of Southern California, Los Angeles, California, USA
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  • For correspondence: sliew@usc.edu
Julia M. Anglin
1University of Southern California, Los Angeles, California, USA
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Nick W. Banks
1University of Southern California, Los Angeles, California, USA
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Matt Sondag
1University of Southern California, Los Angeles, California, USA
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Kaori L. Ito
1University of Southern California, Los Angeles, California, USA
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Hosung Kim
1University of Southern California, Los Angeles, California, USA
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Jennifer Chan
1University of Southern California, Los Angeles, California, USA
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Joyce Ito
1University of Southern California, Los Angeles, California, USA
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Connie Jung
1University of Southern California, Los Angeles, California, USA
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Nima Khoshab
2University of California, Irvine, Irvine, California, USA
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Stephanie Lefebvre
1University of Southern California, Los Angeles, California, USA
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William Nakamura
1University of Southern California, Los Angeles, California, USA
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David Saldana
1University of Southern California, Los Angeles, California, USA
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Allie Schmiesing
1University of Southern California, Los Angeles, California, USA
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Cathy Tran
1University of Southern California, Los Angeles, California, USA
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Danny Vo
1University of Southern California, Los Angeles, California, USA
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Tyler Ard
1University of Southern California, Los Angeles, California, USA
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Panthea Heydari
1University of Southern California, Los Angeles, California, USA
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Bokkyu Kim
1University of Southern California, Los Angeles, California, USA
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Lisa Aziz-Zadeh
1University of Southern California, Los Angeles, California, USA
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Steven C. Cramer
2University of California, Irvine, Irvine, California, USA
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Jingchun Liu
3Tianjin Medical University General Hospital, Tianjin, China
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Surjo Soekadar
4University of Tübingen, Tübingen, Germany
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Jan-Egil Nordvik
5Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
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Lars T. Westlye
6NORMENT and KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
7Department of Psychology, University of Oslo, Oslo, Norway
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Junping Wang
3Tianjin Medical University General Hospital, Tianjin, China
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Carolee Winstein
1University of Southern California, Los Angeles, California, USA
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Chunshui Yu
3Tianjin Medical University General Hospital, Tianjin, China
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Lei Ai
8Child Mind Institute, New York, New York, USA
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Bonhwang Koo
8Child Mind Institute, New York, New York, USA
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R. Cameron Craddock
8Child Mind Institute, New York, New York, USA
9Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, USA
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Michael Milham
8Child Mind Institute, New York, New York, USA
9Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, USA
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Matthew Lakich
10University of Texas Medical Branch, Galveston, Texas, USA
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Amy Pienta
11University of Michigan, Ann Arbor, Michigan
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Alison Stroud
11University of Michigan, Ann Arbor, Michigan
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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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 08, 2017.
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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, Steven C. Cramer, Jingchun Liu, Surjo Soekadar, Jan-Egil Nordvik, Lars T. Westlye, Junping Wang, Carolee Winstein, Chunshui Yu, Lei Ai, Bonhwang Koo, R. Cameron Craddock, Michael Milham, Matthew Lakich, Amy Pienta, Alison Stroud
bioRxiv 179614; doi: https://doi.org/10.1101/179614
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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, Steven C. Cramer, Jingchun Liu, Surjo Soekadar, Jan-Egil Nordvik, Lars T. Westlye, Junping Wang, Carolee Winstein, Chunshui Yu, Lei Ai, Bonhwang Koo, R. Cameron Craddock, Michael Milham, Matthew Lakich, Amy Pienta, Alison Stroud
bioRxiv 179614; doi: https://doi.org/10.1101/179614

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