2016 - IEEE Fellow For contributions to biomedical image computing
2015 - Fellow of the Royal Academy of Engineering (UK)
Fellow of The Academy of Medical Sciences, United Kingdom
Artificial intelligence, Computer vision, Segmentation, Magnetic resonance imaging and Pattern recognition are his primary areas of study. His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Machine learning. His work deals with themes such as Affine transformation, Probabilistic logic, Mutual information and Atlas, which intersect with Computer vision.
His biological study deals with issues like Atlas, which deal with fields such as Pattern recognition. His research in Magnetic resonance imaging intersects with topics in Algorithm, Voxel and Pathology. His Pattern recognition research integrates issues from Neuroimaging, Neuroscience, Cortical surface and Human Connectome Project.
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Magnetic resonance imaging. His research on Artificial intelligence frequently links to adjacent areas such as Machine learning. His Computer vision research is multidisciplinary, incorporating elements of Atlas, Mr images and Atlas.
The Pattern recognition study combines topics in areas such as Feature, Metric, Artificial neural network, Image and Voxel. His Segmentation research is multidisciplinary, relying on both Similarity and Medical imaging. His Magnetic resonance imaging research incorporates themes from Pathology, Internal medicine, Neuroimaging and Cardiology.
Daniel Rueckert mostly deals with Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Magnetic resonance imaging. Daniel Rueckert has researched Artificial intelligence in several fields, including Machine learning and Computer vision. His Pattern recognition research is multidisciplinary, incorporating perspectives in Domain, Feature, Metric, Medical imaging and Image.
His study in Deep learning is interdisciplinary in nature, drawing from both Image quality, Ground truth, Interpretability and k-space. Daniel Rueckert combines subjects such as Mr images, Feature extraction, Discriminative model, Generative model and Test set with his study of Segmentation. The concepts of his Magnetic resonance imaging study are interwoven with issues in Internal medicine, Iterative reconstruction and Cardiology.
His primary scientific interests are in Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Magnetic resonance imaging. His study ties his expertise on Machine learning together with the subject of Artificial intelligence. His work carried out in the field of Pattern recognition brings together such families of science as Feature, Metric, Mr images, Image and Atlas.
His study focuses on the intersection of Segmentation and fields such as Modality with connections in the field of Fetal Skull. His Deep learning study also includes
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Nonrigid registration using free-form deformations: application to breast MR images
D. Rueckert;L.I. Sonoda;C. Hayes;D.L.G. Hill.
IEEE Transactions on Medical Imaging (1999)
Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.
S M Smith;M Jenkinson;H Johansen-Berg;D Rueckert.
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.
Arno Klein;Jesper L. R. Andersson;Babak A. Ardekani;Babak A. Ardekani;John Ashburner.
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi;Jose Caballero;Ferenc Huszar;Johannes Totz.
computer vision and pattern recognition (2016)
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
Konstantinos Kamnitsas;Christian Ledig;Virginia F.J. Newcombe;Joanna P. Simpson.
Medical Image Analysis (2017)
Automatic anatomical brain MRI segmentation combining label propagation and decision fusion.
Rolf A. Heckemann;Joseph V. Hajnal;Paul Aljabar;Daniel Rueckert.
Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy
Paul Aljabar;Rolf A. Heckemann;Alexander Hammers;Joseph V. Hajnal.
Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research
Andrew I R Maas;David K Menon;P David Adelson;Nada Andelic.
Lancet Neurology (2017)
Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration
D. Rueckert;A.F. Frangi;J.A. Schnabel.
IEEE Transactions on Medical Imaging (2003)
Acquisition and voxelwise analysis of multi-subject diffusion data with Tract-Based Spatial Statistics
Stephen M Smith;Heidi Johansen-Berg;Mark Jenkinson;Daniel Rueckert.
Nature Protocols (2007)
Profile was last updated on December 6th, 2021.
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