A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.

Autor:	Liew SL; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA. sliew@usc.edu.; Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. sliew@usc.edu., Lo BP; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA., Donnelly MR; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA., Zavaliangos-Petropulu A; Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Jeong JN; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA., Barisano G; Laboratory of Neuroimaging, Mark and Mary Stevens Neuroimaging and Informatics Institutes, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.; Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA., Hutton A; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA., Simon JP; Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Juliano JM; Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA., Suri A; Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA., Wang Z; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA., Abdullah A; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA., Kim J; Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Ard T; Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Banaj N; Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy., Borich MR; Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, USA., Boyd LA; Department of Physical Therapy & Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada., Brodtmann A; Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia., Buetefisch CM; Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, USA.; Department of Neurology, Emory University, Atlanta, GA, USA., Cao L; Center for the Developing Brain, Child Mind Institute, New York, NY, USA., Cassidy JM; Department of Allied Health Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Ciullo V; Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy., Conforto AB; Hospital das Clínicas, São Paulo University, Sao Paulo, SP, Brazil.; Hospital Israelita Albert Einstein, Sao Paulo, SP, Brazil., Cramer SC; Department of Neurology, University of California Los Angeles and California Rehabilitation Institute, Los Angeles, CA, USA., Dacosta-Aguayo R; Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Barcelona, Spain., de la Rosa E; icometrix, Leuven, Belgium.; Department of Computer Science, Technical University of Munich, Munich, Germany., Domin M; Functional Imaging Unit, Department of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany., Dula AN; Departments of Neurology and Diagnostic Medicine, Dell Medical School at The University of Texas Austin, Austin, TX, USA., Feng W; Department of Neurology, Duke University School of Medicine, Durham, NC, USA., Franco AR; Center for the Developing Brain, Child Mind Institute, New York, NY, USA.; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.; Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA., Geranmayeh F; Department of Brain Sciences, Imperial College London, London, UK., Gramfort A; Center for Data Science, Université Paris-Saclay, Inria, Palaiseau, France., Gregory CM; Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA., Hanlon CA; Cancer Biology, Wake Forest School of Medicine, Winston Salem, NC, USA., Hordacre BG; Innovation, Implementation and Clinical Translation (IIMPACT) in Health, Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia., Kautz SA; Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA.; Ralph H Johnson VA Medical Center, Charleston, SC, USA., Khlif MS; The Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia., Kim H; Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Kirschke JS; Neuroradiology, School of Medicine, Technical University Munich, München, Germany., Liu J; Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China., Lotze M; Functional Imaging Unit, Department of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany., MacIntosh BJ; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.; Hurvitz Brain Sciences Program, Toronto, Ontario, Canada., Mataró M; Department of Clinical Psychology and Psychobiology, Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain.; Institut de Recerca Sant Joan de Déu, 08950, Esplugues de Llobregat, Spain., Mohamed FB; Jefferson Magnetic Resonance Imaging Center, Philadelphia, PA, USA., Nordvik JE; CatoSenteret Rehabilitation Center, SON, Norway.; Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway., Park G; Laboratory of Neuroimaging, Mark and Mary Stevens Neuroimaging and Informatics Institutes, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Pienta A; Inter-university Consortium for Political and Social Research, University of Michigan, Ann Arbor, MI, USA., Piras F; Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy., Redman SM; Inter-university Consortium for Political and Social Research, University of Michigan, Ann Arbor, MI, USA., Revill KP; Facility for Education and Research in Neuroscience, Emory University, Atlanta, GA, USA., Reyes M; ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland., Robertson AD; Schlegel-University of Waterloo Research Institute for Aging, University of Waterloo, Waterloo, Ontario, Canada.; Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, Toronto, Ontario, Canada., Seo NJ; Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA.; Ralph H Johnson VA Medical Center, Charleston, SC, USA.; Department of Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC, USA., Soekadar SR; Clinical Neurotechnology Laboratory, Dept. of Psychiatry and Neurosciences (CCM), Charité - Universitätsmedizin Berlin, Berlin, Germany., Spalletta G; Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.; Menninger Department of Psychiatry and Behavioral Sciences, Division of Neuropsychiatry, Baylor College of Medicine, Houston, TX, USA., Sweet A; Inter-university Consortium for Political and Social Research, University of Michigan, Ann Arbor, MI, USA., Telenczuk M; Center for Data Science, Université Paris-Saclay, Inria, Palaiseau, France., Thielman G; Department of Physical Therapy and Neuroscience, Samson College of Health Sciences, St. Joseph's University, Philadelphia, PA, USA., Westlye LT; Department of Psychology, University of Oslo, Oslo, Norway.; NORMENT, Department of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway., Winstein CJ; Division of Biokinesiology and Physical Therapy of the Herman Ostrow School of Dentistry, University of Southern California, Los Angeles, CA, USA.; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Wittenberg GF; Geriatrics Research, Education and Clinical Center, HERL, Department of Veterans Affairs, Pittsburgh, PA, USA.; Departments of Neurology, PM&R, RNEL, CNBC, University of Pittsburgh, Pittsburgh, PA, USA., Wong KA; Department of Physical Medicine & Rehabilitation, Dell Medical School, University of Texas at Austin, Austin, TX, USA., Yu C; Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China.; Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
Jazyk:	angličtina
Zdroj:	Scientific data [Sci Data] 2022 Jun 16; Vol. 9 (1), pp. 320. Date of Electronic Publication: 2022 Jun 16.
DOI:	10.1038/s41597-022-01401-7
Abstrakt:	Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research. (© 2022. The Author(s).)
Databáze:	MEDLINE
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