V
Vladimir S. Fonov
Researcher at Montreal Neurological Institute and Hospital
Publications - 191
Citations - 11259
Vladimir S. Fonov is an academic researcher from Montreal Neurological Institute and Hospital. The author has contributed to research in topics: Population & Segmentation. The author has an hindex of 44, co-authored 178 publications receiving 8719 citations. Previous affiliations of Vladimir S. Fonov include Heriot-Watt University & Children's Hospital of Philadelphia.
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Journal ArticleDOI
Unbiased Average Age-Appropriate Atlases for Pediatric Studies
Vladimir S. Fonov,Alan C. Evans,Kelly N. Botteron,C. Robert Almli,Robert C. McKinstry,D. Louis Collins +5 more
TL;DR: The methods used to create unbiased, age-appropriate MRI atlas templates for pediatric studies that represent the average anatomy for the age range of 4.5-18.5 years are presented, while maintaining a high level of anatomical detail and contrast.
Journal ArticleDOI
Unbiased nonlinear average age-appropriate brain templates from birth to adulthood
Journal ArticleDOI
Early brain development in infants at high risk for autism spectrum disorder
Heather C. Hazlett,Hongbin Gu,Brent C. Munsell,Sun Hyung Kim,Martin Styner,Jason J. Wolff,Jed T. Elison,Meghan R. Swanson,Hongtu Zhu,Kelly N. Botteron,D. Louis Collins,John N. Constantino,Stephen R. Dager,Annette Estes,Alan C. Evans,Vladimir S. Fonov,Guido Gerig,Penelope Kostopoulos,Robert C. McKinstry,Juhi Pandey,Sarah Paterson,John R. Pruett,Robert T. Schultz,Dennis W. W. Shaw,Lonnie Zwaigenbaum,Joseph Piven +25 more
TL;DR: It is shown that hyperexpansion of the cortical surface area between 6 and 12 months of age precedes brain volume overgrowth observed between 12 and 24 months in 15 high-risk infants who were diagnosed with autism at 24 months.
Journal ArticleDOI
Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation.
Pierrick Coupé,José V. Manjón,Vladimir S. Fonov,Jens C. Pruessner,Montserrat Robles,D. Louis Collins +5 more
TL;DR: Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation in quantitative magnetic resonance analysis.
Journal ArticleDOI
BEaST: brain extraction based on nonlocal segmentation technique.
Simon Fristed Eskildsen,Pierrick Coupé,Vladimir S. Fonov,José V. Manjón,Kelvin K. Leung,Nicolas Guizard,Shafik N. Wassef,Lasse Riis Østergaard,D. Louis Collins +8 more
TL;DR: A new robust method dedicated to produce consistent and accurate brain extraction based on nonlocal segmentation embedded in a multi-resolution framework, which provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.