His primary areas of study are Magnetic resonance imaging, Artificial intelligence, Neuroimaging, Neuroscience and Pattern recognition. His Magnetic resonance imaging study incorporates themes from Mixed model and Pathology. His research investigates the connection between Artificial intelligence and topics such as Computer vision that intersect with problems in Interpolation.
His Neuroimaging research is multidisciplinary, incorporating elements of Familial risk, Brain development, Linear discriminant analysis, Atlas and Pediatrics. His research integrates issues of Voxel and Entorhinal cortex in his study of Pattern recognition. His work investigates the relationship between Segmentation and topics such as Image processing that intersect with problems in Image segmentation.
Vladimir S. Fonov focuses on Artificial intelligence, Pattern recognition, Magnetic resonance imaging, Segmentation and Neuroscience. In most of his Artificial intelligence studies, his work intersects topics such as Computer vision. His Pattern recognition research integrates issues from Brain morphometry, Outlier, Robustness and Atlas.
His Magnetic resonance imaging research is multidisciplinary, relying on both Neuroimaging, Nuclear medicine and Pathology. The various areas that Vladimir S. Fonov examines in his Segmentation study include Image processing and Convolutional neural network. In his study, Disease, Parkinson's disease, Alzheimer's disease and Multiple sclerosis is inextricably linked to Atrophy, which falls within the broad field of Neuroscience.
Vladimir S. Fonov spends much of his time researching Artificial intelligence, Pattern recognition, Cognition, Magnetic resonance imaging and Neuroimaging. His Artificial intelligence study integrates concerns from other disciplines, such as Atlas and Computer vision. In the subject of general Pattern recognition, his work in Segmentation is often linked to Process, thereby combining diverse domains of study.
His studies in Cognition integrate themes in fields like Internal medicine, Cohort and Audiology. Vladimir S. Fonov has researched Magnetic resonance imaging in several fields, including Brain tumor and Nuclear medicine. The concepts of his Neuroimaging study are interwoven with issues in Cartography, Middle temporal gyrus, Functional magnetic resonance imaging and Prefrontal cortex.
His main research concerns Artificial intelligence, Pattern recognition, Cognition, Magnetic resonance imaging and Segmentation. His work deals with themes such as High spatial resolution, Diagnostic biomarker and Atlas, which intersect with Artificial intelligence. His work carried out in the field of Pattern recognition brings together such families of science as Metadata, Data pre-processing, Brain morphometry, Noise and Relevance.
His Cognition research incorporates elements of Cerebellum, Schizophrenia, Lobe and Cohort. His work on Multi contrast as part of general Magnetic resonance imaging study is frequently linked to In patient, bridging the gap between disciplines. His studies in Segmentation integrate themes in fields like Deep learning, Convolutional neural network, Outlier and Robustness.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Unbiased Average Age-Appropriate Atlases for Pediatric Studies
Vladimir S. Fonov;Alan C. Evans;Kelly N. Botteron;C. Robert Almli.
Unbiased nonlinear average age-appropriate brain templates from birth to adulthood
VS Fonov;AC Evans;RC McKinstry;CR Almli.
Early brain development in infants at high risk for autism spectrum disorder
Heather Cody Hazlett;Hongbin Gu;Brent C. Munsell;Sun Hyung Kim.
Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation.
Pierrick Coupé;José V. Manjón;Vladimir S. Fonov;Jens C. Pruessner.
BEaST: brain extraction based on nonlocal segmentation technique.
Simon Fristed Eskildsen;Pierrick Coupé;Vladimir Fonov;José V. Manjón.
Total and regional brain volumes in a population-based normative sample from 4 to 18 years: The NIH MRI study of normal brain development
W. S. Ball;A. W. Byars;M. Schapiro;W. Bommer.
Cerebral Cortex (2012)
SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data
Benjamin De Leener;Simon Lévy;Sara M. Dupont;Vladimir S. Fonov.
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge
Esther E. Bron;Marion Smits;Wiesje M. van der Flier;Hugo Vrenken.
Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning.
Simon F. Eskildsen;Pierrick Coupé;Daniel García-Lorenzo;Vladimir S. Fonov.
Non-local MRI upsampling.
José V. Manjón;Pierrick Coupé;Antonio Buades;Vladimir S. Fonov.
Medical Image Analysis (2010)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: