Anatomical Tracings of Lesions After Stroke (ATLAS)

Overview

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 stroke recovery. However, analyzing large datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation, 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 229 T1-weighted MRI scans (n=220) 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 R1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

The dataset includes:

229 T1-weighted MRI scans (n=220) with lesion segmentation
MNI152 standard-space T1-weighted average structural template image
A .csv file containing lesion metadata

Data Release Download

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bioRxiv Preprint

Click here to access the bioRxiv preprint for ATLAS R1.1.

doi: https://doi.org/10.1101/179614

Authors

Sook-Lei Liew^1†*, Julia M. Anglin^1*, Nick W. Banks¹, Matt Sondag¹, Kaori L. Ito¹, Hosung Kim¹, Jennifer Chan¹, Joyce Ito¹, Connie Jung¹, Stephanie Lefebvre¹, William Nakamura¹, David Saldana¹, Allie Schmiesing¹, Cathy Tran¹, Danny Vo¹, Tyler Ard¹, Panthea Heydari¹, Bokkyu Kim¹, Lisa Aziz-Zadeh¹, Steven C. Cramer², Jingchun Liu³, Surjo Soekadar⁴, Jan-Egil Nordvik⁵, Lars T. Westlye^6,7, Junping Wang³, Carolee Winstein¹, Chunshui Yu³, Lei Ai⁸, Bonhwang Koo⁸, R. Cameron Craddock^8,9, Michael Milham^8,9, Matthew Lakich¹⁰, Amy Pienta¹¹, Alison Stroud¹¹

Corresponding author: Sook-Lei Liew (sliew @ usc.edu)

^*Denotes equal contributions

Affiliations

University of Southern California, Los Angeles, California, USA
University of California, Irvine, Irvine, California, USA
Tianjin Medical University General Hospital, Tianjin, China
University of Tübingen, Tübingen, Germany
Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
NORMENT and KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
Department of Psychology, University of Oslo, Oslo, Norway
Child Mind Institute, New York, New York, USA
Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, USA
University of Texas Medical Branch, Galveston, Texas, USA
University of Michigan, Ann Arbor, Michigan, USA

Acknowledgements

We would like to acknowledge the following people for their assistance on this effort: Anthony Benitez, Xiaoyu Chen, Cristi Magracia, Ryan Mori, Dhanashree Potdar, Sandyha Prathap.