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A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.
Liew, Sook-Lei; Lo, Bethany P; Donnelly, Miranda R; Zavaliangos-Petropulu, Artemis; Jeong, Jessica N; Barisano, Giuseppe; Hutton, Alexandre; Simon, Julia P; Juliano, Julia M; Suri, Anisha; Wang, Zhizhuo; Abdullah, Aisha; Kim, Jun; Ard, Tyler; Banaj, Nerisa; Borich, Michael R; Boyd, Lara A; Brodtmann, Amy; Buetefisch, Cathrin M; Cao, Lei; Cassidy, Jessica M; Ciullo, Valentina; Conforto, Adriana B; Cramer, Steven C; Dacosta-Aguayo, Rosalia; de la Rosa, Ezequiel; Domin, Martin; Dula, Adrienne N; Feng, Wuwei; Franco, Alexandre R; Geranmayeh, Fatemeh; Gramfort, Alexandre; Gregory, Chris M; Hanlon, Colleen A; Hordacre, Brenton G; Kautz, Steven A; Khlif, Mohamed Salah; Kim, Hosung; Kirschke, Jan S; Liu, Jingchun; Lotze, Martin; MacIntosh, Bradley J; Mataró, Maria; Mohamed, Feroze B; Nordvik, Jan E; Park, Gilsoon; Pienta, Amy; Piras, Fabrizio; Redman, Shane M; Revill, Kate P.
Afiliación
  • Liew SL; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA. sliew@usc.edu.
  • Lo BP; Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. sliew@usc.edu.
  • Donnelly MR; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
  • Zavaliangos-Petropulu A; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
  • Jeong JN; Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Barisano G; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
  • Hutton A; Laboratory of Neuroimaging, Mark and Mary Stevens Neuroimaging and Informatics Institutes, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Simon JP; Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.
  • Juliano JM; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
  • Suri A; Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Wang Z; Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.
  • Abdullah A; Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
  • Kim J; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
  • Ard T; Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
  • Banaj N; Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Borich MR; Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Boyd LA; Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.
  • Brodtmann A; Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, USA.
  • Buetefisch CM; Department of Physical Therapy & Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.
  • Cao L; Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia.
  • Cassidy JM; Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, USA.
  • Ciullo V; Department of Neurology, Emory University, Atlanta, GA, USA.
  • Conforto AB; Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
  • Cramer SC; Department of Allied Health Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Dacosta-Aguayo R; Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.
  • de la Rosa E; Hospital das Clínicas, São Paulo University, Sao Paulo, SP, Brazil.
  • Domin M; Hospital Israelita Albert Einstein, Sao Paulo, SP, Brazil.
  • Dula AN; Department of Neurology, University of California Los Angeles and California Rehabilitation Institute, Los Angeles, CA, USA.
  • Feng W; Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Barcelona, Spain.
  • Franco AR; icometrix, Leuven, Belgium.
  • Geranmayeh F; Department of Computer Science, Technical University of Munich, Munich, Germany.
  • Gramfort A; Functional Imaging Unit, Department of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany.
  • Gregory CM; Departments of Neurology and Diagnostic Medicine, Dell Medical School at The University of Texas Austin, Austin, TX, USA.
  • Hanlon CA; Department of Neurology, Duke University School of Medicine, Durham, NC, USA.
  • Hordacre BG; Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
  • Kautz SA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
  • Khlif MS; Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA.
  • Kim H; Department of Brain Sciences, Imperial College London, London, UK.
  • Kirschke JS; Center for Data Science, Université Paris-Saclay, Inria, Palaiseau, France.
  • Liu J; Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA.
  • Lotze M; Cancer Biology, Wake Forest School of Medicine, Winston Salem, NC, USA.
  • MacIntosh BJ; Innovation, Implementation and Clinical Translation (IIMPACT) in Health, Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia.
  • Mataró M; Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA.
  • Mohamed FB; Ralph H Johnson VA Medical Center, Charleston, SC, USA.
  • Nordvik JE; The Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia.
  • Park G; Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Pienta A; Neuroradiology, School of Medicine, Technical University Munich, München, Germany.
  • Piras F; Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China.
  • Redman SM; Functional Imaging Unit, Department of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany.
  • Revill KP; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Sci Data ; 9(1): 320, 2022 06 16.
Article en En | MEDLINE | ID: mdl-35710678
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Accidente Cerebrovascular Límite: Humans Idioma: En Revista: Sci Data Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Accidente Cerebrovascular Límite: Humans Idioma: En Revista: Sci Data Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos