H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 103 Citations 56,288 626 World Ranking 126 National Ranking 9

Research.com Recognitions

Awards & Achievements

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

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Magnetic resonance imaging
  • Computer vision

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 most cited work include:

  • Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. (4658 citations)
  • Nonrigid registration using free-form deformations: application to breast MR images (4302 citations)
  • Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (2042 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (69.75%)
  • Computer vision (33.40%)
  • Pattern recognition (29.10%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (69.75%)
  • Pattern recognition (29.10%)
  • Deep learning (11.34%)

In recent papers he was focusing on the following fields of study:

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.

Between 2017 and 2021, his most popular works were:

  • Attention U-Net: Learning Where to Look for the Pancreas (581 citations)
  • A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction (520 citations)
  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (493 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Magnetic resonance imaging
  • Statistics

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

  • Representation which connect with Pipeline,
  • Compressed sensing and related Undersampling and Ground truth. Daniel Rueckert works mostly in the field of Magnetic resonance imaging, limiting it down to topics relating to Cognition and, in certain cases, Hyperintensity and Cognitive decline.

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.

Top Publications

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)

5624 Citations

Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.

S M Smith;M Jenkinson;H Johansen-Berg;D Rueckert.
NeuroImage (2006)

5341 Citations

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.
NeuroImage (2009)

2024 Citations

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)

1604 Citations

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)

1459 Citations

Automatic anatomical brain MRI segmentation combining label propagation and decision fusion.

Rolf A. Heckemann;Joseph V. Hajnal;Paul Aljabar;Daniel Rueckert.
NeuroImage (2006)

978 Citations

Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy

Paul Aljabar;Rolf A. Heckemann;Alexander Hammers;Joseph V. Hajnal.
NeuroImage (2009)

909 Citations

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)

550 Citations

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)

540 Citations

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)

536 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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