{"id":2290,"date":"2018-02-21T10:41:56","date_gmt":"2018-02-21T18:41:56","guid":{"rendered":"https:\/\/keck.usc.edu\/news\/shared-database-of-brain-images-aims-to-boost-stroke-patients-recovery\/"},"modified":"2023-11-26T08:24:11","modified_gmt":"2023-11-26T16:24:11","slug":"shared-database-of-brain-images-aims-to-boost-stroke-patients-recovery","status":"publish","type":"post","link":"https:\/\/keck.usc.edu\/news\/shared-database-of-brain-images-aims-to-boost-stroke-patients-recovery\/","title":{"rendered":"Shared database of brain images aims to boost stroke patients\u2019 recovery"},"content":{"rendered":"\n \n \n\n\n\n\n\n\n\n
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\n\n \n Press Release<\/span>\n\n\n\n<\/div>\n \n \n
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Shared database of brain images aims to boost stroke patients\u2019 recovery<\/h1>\n\n\n<\/div>\n \n \n
\n \n February 21, 2018<\/span>\n <\/div>\n \n \n
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This image depicts the distribution of lesions in the ATLAS data set; the redder the color, the more patients had lesions in that region. (Image\/Courtesy of INI,Tyler Ard)<\/figcaption>\n <\/figure>\n \n \n \n\n<\/div>\n \n\n <\/div><\/div>\n\r\n\r\n\n\n \n \n\n\n\n\n\n\n\n
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Brain scans from stroke patients are being downloaded by researchers around the world to predict the most efficient therapies
\nA USC-led team has archived and shared hundreds of brain scans from stroke patients, which researchers hope will help to forecast which patients will respond to a variety of rehabilitation therapies.
\nThe study of the data set, known as Anatomical Tracings of Lesion After Stroke (ATLAS), was published in\u00a0Scientific Data<\/a>, a Nature journal.
\nStroke is the leading cause of disability in adults, affecting more than 15 million people worldwide each year, according to the\u00a0World Health Organization<\/a>. During a stroke, blood flow to part of the brain is cut off. Without oxygen, brain cells die and cease to function. The damaged area, known as a lesion, is what researchers and clinicians study as they design, test and implement recovery programs. Typically, neuroanatomy experts manually draw boundaries around the lesions \u2014 in a process called segmentation \u2014 but researchers hope to automate this practice so they can examine more images.
\n\u201cOne of our goals is to meta-analyze thousands of stroke MRIs from around the world to understand how the lesions impact recovery,\u201d said\u00a0Sook-Lei Liew<\/a>, lead author of the study and assistant professor with joint appointments at the\u00a0Mark and Mary Stevens Neuroimaging and Informatics Institute<\/a>\u00a0(INI) within the Keck School of Medicine of USC, the\u00a0Chan Division of Occupational Science and Occupational Therapy<\/a>, the\u00a0Division of Biokinesiology and Physical Therapy<\/a>\u00a0and the\u00a0USC Viterbi School of Engineering<\/a>.
\n\u201cWe can\u2019t do it by hand at the scale of thousands, so we are really interested in helping find better automated ways, using machine learning and computer vision, to identify the lesions and have machines draw those boundaries.\u201d
\n\u201cDr. Liew\u2019s team is making great strides toward improving patient outcomes following stroke,\u201d said Provost Professor\u00a0Arthur Toga<\/a>, the INI director. \u201cSeveral other faculty from the institute and across the university have applied their expertise in machine learning, data visualization, informatics and neuroradiology to deliver a valuable set of open-source MR images.\u201d<\/p>\n

A collaborative effort<\/h3>\n

The ATLAS team represents a collaborative effort both within USC and beyond.\u00a0Hosung Kim<\/a>, assistant professor of neurology at INI, used a neuroimaging analysis pipeline he developed to help standardize the images in the data set. The institute\u2019s Tyler Ard, assistant professor of research, created custom software for advanced visualization of the lesioned data set, rendering it into several extremely high-resolution videos and images. Seventeen other co-authors across the university assisted with analysis, clinical characterization, and the collection and storage of data.
\nData from the project are stored by the International Neuroimaging Data-Sharing Initiative (INDI), housed at the Child Mind Institute, and by the Inter-University Consortium for Political and Social Research (ICPSR), housed at the University of Michigan. So far, 33 research groups around the world, including from Finland, Iran and Australia, have downloaded the ATLAS data set, which contains 304 manually-segmented MRI scans.
\nLiew and Kim, along with PhD student Kaori Ito, have already started putting the data set to work. They\u2019re testing all of the existing algorithms that attempt to automate the lesion segmentation process to determine which perform the task with greatest accuracy. They presented their work at the annual meeting of the American Society for Neurorehabilitation in November and currently have a paper under review.<\/p>\n

The long-term goal<\/h3>\n

As predictive algorithms improve, a long-term goal is for clinicians to use MRI to inform decisions about stroke patients\u2019 treatment and recovery.
\n\u201cUltimately, we would run their data through an automated pipeline that would give us some measures of their likelihood of recovery, or more importantly, their likelihood of responding to different types of therapies,\u201d Liew said. \u201cWe could then personalize their rehabilitation therapy based on their MRI results and, hopefully, improve their recovery.\u201d
\nStroke researchers who wish to access the data can download a\u00a0normalized subset<\/a>\u00a0(n=229) from INDI or the\u00a0full data set<\/a>\u00a0(n=304) from ICPSR.
\nThe research was funded by the National Institutes of Health-funded Center for Large Data Research and Data Sharing in Rehabilitation under a Category 2 Pilot Grant (P2CHD06570) and by a National Institutes of Health K01 award from the National Center for Medical Rehabilitation Research (K01HD091283).<\/p>\n\n\n\n<\/div>\n <\/div>\n\n\n <\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":168,"featured_media":2291,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"advgb_blocks_editor_width":"","advgb_blocks_columns_visual_guide":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[6],"tags":[521],"class_list":["post-2290","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-press-release","tag-mesh-strategic-partnerships"],"acf":[],"yoast_head":"\nShared database of brain images aims to boost stroke patients\u2019 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