Daniel Rueckert
Impact in
- Radiology, Nuclear Medicine and Imaging top 0.01%
- Advanced Neuroimaging Techniques and Applications
- Advanced MRI Techniques and Applications
- Medical Imaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- Computer Vision and Pattern Recognition top 0.01%
- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
Papers in
-
- Advanced MRI Techniques and Applications 197
- Medical Imaging Techniques and Applications 130
- Advanced Neuroimaging Techniques and Applications 101
- Radiomics and Machine Learning in Medical Imaging 78
- Cardiac Imaging and Diagnostics 66
-
- Medical Image Segmentation Techniques 296
- Advanced Neural Network Applications 60
- Co-authors
- Joseph V. Hajnal (153 shared papers)David J. Hawkes (24 shared papers)José Caballero (13 shared papers)Paul Aljabar (78 shared papers)Ben Glocker (58 shared papers)David Hill (42 shared papers)Wenzhe Shi (49 shared papers)Carmel Hayes (4 shared papers)
- Journals
- IEEE Transactions on Medical Imaging (59 papers)NeuroImage (45 papers)Medical Image Analysis (37 papers)Lecture notes in computer science (301 papers)PLoS ONE (11 papers)
- Partner nations
- United KingdomGermanyUnited States
In The Last Decade
Daniel Rueckert
827 papers receiving 54.9k citations
Daniel Rueckert's Hit Papers
Peers
Comparison fields: 5 of 214
- Radiology, Nuclear Medicine and Imaging 21.8k
- Computer Vision and Pattern Recognition 21.2k
- Neurology 4.8k
- Health Informatics 807
- Media Technology 3.1k
Countries citing papers authored by Daniel Rueckert
This map shows the geographic impact of Daniel Rueckert's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Rueckert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Rueckert more than expected).
Fields of papers citing papers by Daniel Rueckert
This network shows the impact of papers produced by Daniel Rueckert. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Rueckert. The network helps show where Daniel Rueckert may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Rueckert, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 871 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data Hit paper breakdown → | 2006 | 5441 |
| 2 | Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Hit paper breakdown → | 2016 | 4794 |
| 3 | Nonrigid registration using free-form deformations: application to breast MR images Hit paper breakdown → | 1999 | 4275 |
| 4 | Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Hit paper breakdown → | 2016 | 2511 |
| 5 | Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration Hit paper breakdown → | 2009 | 1784 |
| 6 | Attention gated networks: Learning to leverage salient regions in medical images Hit paper breakdown → | 2019 | 1380 |
| 7 | Medical Image Computing and Computer-Assisted Intervention Hit paper breakdown → | 2009 | 1336 |
| 8 | A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction Hit paper breakdown → | 2017 | 938 |
| 9 | Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy Hit paper breakdown → | 2009 | 729 |
| 10 | Automatic anatomical brain MRI segmentation combining label propagation and decision fusion Hit paper breakdown → | 2006 | 713 |
| 11 | Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation Hit paper breakdown → | 2017 | 524 |
| 12 | Acquisition and voxelwise analysis of multi-subject diffusion data with Tract-Based Spatial Statistics Hit paper breakdown → | 2007 | 507 |
| 13 | Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease Hit paper breakdown → | 2018 | 506 |
| 14 | Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction Hit paper breakdown → | 2018 | 447 |
| 15 | Self-supervised learning for medical image analysis using image context restoration Hit paper breakdown → | 2019 | 391 |
| 16 | 2012 | 367 | |
| 17 | 2003 | 346 | |
| 18 | 2009 | 321 | |
| 19 | 2009 | 320 | |
| 20 | 2002 | 316 |
About Daniel Rueckert
Daniel Rueckert is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering and Pediatrics, Perinatology and Child Health, having authored 871 papers that have together received 56.1k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (296 papers), Advanced MRI Techniques and Applications (197 papers), Medical Imaging Techniques and Applications (130 papers), Advanced Neuroimaging Techniques and Applications (101 papers), Radiomics and Machine Learning in Medical Imaging (78 papers), Fetal and Pediatric Neurological Disorders (67 papers), Cardiac Imaging and Diagnostics (66 papers) and Advanced Neural Network Applications (60 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (21.8k citations), Computer Vision and Pattern Recognition (21.2k citations), Neurology (4.8k citations), Health Informatics (807 citations) and Media Technology (3.1k citations). Daniel Rueckert has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Joseph V. Hajnal, David J. Hawkes, José Caballero, Paul Aljabar, Ben Glocker, David Hill, Wenzhe Shi, Carmel Hayes, Martin O. Leach and Luke Sonoda. Their work appears in journals such as IEEE Transactions on Medical Imaging, NeuroImage, Medical Image Analysis, Lecture notes in computer science and PLoS ONE.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.