His primary areas of study are Artificial intelligence, Segmentation, Magnetic resonance imaging, Pathology and Computer vision. His Artificial intelligence research incorporates elements of Machine learning and Pattern recognition. Sebastien Ourselin focuses mostly in the field of Segmentation, narrowing it down to matters related to Data mining and, in some cases, Categorical variable.
His Magnetic resonance imaging research is multidisciplinary, incorporating elements of Positron emission tomography, Nuclear medicine, Neuroimaging and Brain mapping. The various areas that Sebastien Ourselin examines in his Pathology study include Voxel-based morphometry and Neuroscience. His Neuroscience research incorporates themes from White matter and Disease.
His main research concerns Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Magnetic resonance imaging. Many of his studies on Artificial intelligence apply to Machine learning as well. His Computer vision research includes themes of Imaging phantom, Robustness and Medical imaging.
Sebastien Ourselin mostly deals with Scale-space segmentation in his studies of Segmentation. Sebastien Ourselin interconnects Positron emission tomography, Nuclear medicine, Atrophy, Pathology and Neuroimaging in the investigation of issues within Magnetic resonance imaging. His studies deal with areas such as Alzheimer's disease and Frontotemporal dementia as well as Atrophy.
Sebastien Ourselin focuses on Artificial intelligence, Pattern recognition, Segmentation, Convolutional neural network and Magnetic resonance imaging. His research integrates issues of Machine learning and Computer vision in his study of Artificial intelligence. Sebastien Ourselin has researched Pattern recognition in several fields, including Domain, Modality, Neuroimaging and Medical imaging.
His Magnetic resonance imaging research is under the purview of Radiology.
His primary areas of study are Artificial intelligence, Segmentation, Pattern recognition, Disease and Internal medicine. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Computer vision. His Segmentation research focuses on Overfitting and how it relates to Algorithm and Set.
Sebastien Ourselin does research in Pattern recognition, focusing on Image segmentation specifically. His Frontotemporal dementia study in the realm of Disease interacts with subjects such as Anosmia. His study in Convolutional neural network is interdisciplinary in nature, drawing from both Magnetic resonance imaging and Dice.
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Fast free-form deformation using graphics processing units
Marc Modat;Gerard R. Ridgway;Zeike A. Taylor;Manja Lehmann.
Computer Methods and Programs in Biomedicine (2010)
Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
Carole H. Sudre;Carole H. Sudre;Wenqi Li;Tom Vercauteren;Sebastien Ourselin;Sebastien Ourselin.
3rd MICCAI International Workshop on Deep Learning in Medical Image Analysis (DLMIA) / 7th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS), Date: 2017/09/14, Location: Quebec, CANADA (2017)
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer.
Unknown Journal (2018)
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
Nicholas Ayache;Sébastien Ourselin;Anthony Maeder.
Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study.
Long H. Nguyen;David A. Drew;Mark S. Graham;Amit D. Joshi.
The Lancet. Public health (2020)
Reconstructing a 3D structure from serial histological sections
Sébastien Ourselin;Alexis Roche;Gérard Subsol;Xavier Pennec.
Image and Vision Computing (2001)
Real-time tracking of self-reported symptoms to predict potential COVID-19.
Cristina Menni;Ana M Valdes;Ana M Valdes;Maxim B Freidin;Carole H Sudre.
Nature Medicine (2020)
Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge
K. Murphy;B. van Ginneken;J. M. Reinhardt;S. Kabus.
IEEE Transactions on Medical Imaging (2011)
NiftyNet: a deep-learning platform for medical imaging
Eli Gibson;Wenqi Li;Carole H. Sudre;Lucas Fidon.
Computer Methods and Programs in Biomedicine (2018)
Head size, age and gender adjustment in MRI studies: a necessary nuisance?
Josephine Barnes;Gerard R. Ridgway;Gerard R. Ridgway;Jonathan W. Bartlett;Susie M. D. Henley.
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