USC releases MRI stroke dataset to spur AI research

The University of Southern California has made available one of the largest open-source datasets of brain scans from stroke patients in a push to spur the development of machine learning to automatically process MRI images and identify lesions. The Anatomical Tracings of Lesion After Stroke (ATLAS) dataset, which contains 304 manually segmented MRI scans that took more than 500 hours to create, is now available for download to researchers around the world. "The unique thing is that we have manually traced the lesions on all of these brains--304 brains in total," says Sook-Lei Liew, assistant professor with joint appointments at the USC Mark and Mary Stevens Neuroimaging and Informatics Institute, the Chan Division of Occupational Science and Occupational Therapy, the Division of Biokinesiology and Physical Therapy, and the USC Viterbi School of Engineering. According to Liew, manually traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are time consuming and require neuroanatomy expertise. And while algorithms that employ machine-learning techniques hold promise for automating the process, it requires large training datasets to optimize performance, she contends.