World's Best Scientists 2026 revealed!
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Computer Science
Germany
2026

D-Index & Metrics

Computer Science

D-Index
132
Citations
93473
World Ranking
96
National Ranking
8

Research.com Recognitions

  • 2026 - Research.com Computer Science in Germany Leader Award
  • 2025 - Research.com Computer Science in Germany Leader Award
  • 2023 - Research.com Computer Science in Germany Leader Award
  • 2022 - Research.com Computer Science in Germany Leader Award
  • 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
  • Fellow of The Academy of Medical Sciences, United Kingdom
  • Fellow of The Academy of Medical Sciences, United Kingdom

Overview

Daniel Rueckert is affiliated with the Technical University of Munich in Germany, focusing primarily on research at the intersection of medicine and computer science. Their work encompasses various subfields including radiology, nuclear medicine and imaging, artificial intelligence, computer vision and pattern recognition, pediatrics, perinatology and child health, as well as biomedical engineering.

Their research topics cover a range of areas in medical imaging and analysis, notably:

  • Advanced MRI Techniques and Applications
  • Radiomics and Machine Learning in Medical Imaging
  • Medical Imaging Techniques and Applications
  • Medical Image Segmentation Techniques
  • Advanced Neuroimaging Techniques and Applications
  • Privacy-Preserving Technologies in Data
  • Medical Imaging and Analysis

Recent publications demonstrate engagement with both methodological advances and clinical applications. Notable papers include:

  • "Evaluation and mitigation of the limitations of large language models in clinical decision-making" (2024, Nature Medicine)
  • "End-to-end privacy preserving deep learning on multi-institutional medical imaging" (2021, Nature Machine Intelligence)
  • "Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study" (2021, npj Digital Medicine)
  • "A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging" (2020, Medical Image Analysis)
  • "A population-based phenome-wide association study of cardiac and aortic structure and function" (2020, Nature Medicine)

The scientist frequently collaborates with other researchers, including Georgios Kaissis, Kerstin Hammernik, Joseph V. Hajnal, Benedikt Wiestler, and Wenjia Bai. These coauthors appear regularly across their body of work.

Publication venues for their research include:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
  • Medical Image Analysis

In terms of book contributions, Daniel Rueckert has authored works published by Springer Science+Business Media and IntechOpen. Titles include "Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning" (2021) and "Gamification - Analysis, Design, Development and Ludification" (2022).

Awards received include recognition as an IEEE Fellow in 2016 for contributions to biomedical image computing, Fellow of the Royal Academy of Engineering (UK) in 2015, and Fellowship of The Academy of Medical Sciences, United Kingdom.

Best Publications

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

    S M Smith;M Jenkinson;H Johansen-Berg;D Rueckert

  • Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

    Wenzhe Shi;Jose Caballero;Ferenc Huszar;Johannes Totz

  • Nonrigid registration using free-form deformations: application to breast MR images

    D. Rueckert;L.I. Sonoda;C. Hayes;D.L.G. Hill

  • Attention U-Net: Learning Where to Look for the Pancreas

    Ozan Oktay;Jo Schlemper;Loïc Le Folgoc;Matthew C. H. Lee

  • 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

  • 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

  • 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

  • Attention gated networks: Learning to leverage salient regions in medical images.

    Jo Schlemper;Ozan Oktay;Michiel Schaap;Mattias P. Heinrich

  • A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

    S. Kevin Zhou;Hayit Greenspan;Christos Davatzikos;James S. Duncan

  • A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

    Jo Schlemper;Jose Caballero;Joseph V. Hajnal;Anthony N. Price

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

    Paul Aljabar;Rolf A. Heckemann;Alexander Hammers;Joseph V. Hajnal

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

    Rolf A. Heckemann;Joseph V. Hajnal;Paul Aljabar;Daniel Rueckert

  • Deep Learning for Cardiac Image Segmentation: A Review.

    Chen Chen;Chen Qin;Huaqi Qiu;Giacomo Tarroni;Giacomo Tarroni

  • Medical Image Computing and Computer-Assisted Intervention

    G Z Yang;D Hawkes;D Rueckert;J A Noble

  • Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

  • Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

    Wenjia Bai;Matthew Sinclair;Giacomo Tarroni;Ozan Oktay

  • Secure, privacy-preserving and federated machine learning in medical imaging

    Georgios A. Kaissis;Georgios A. Kaissis;Marcus R. Makowski;Daniel Rückert;Rickmer F. Braren

  • Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease.

    Sarah Parisot;Sofia Ira Ktena;Enzo Ferrante;Matthew C. H. Lee

  • Acquisition and voxelwise analysis of multi-subject diffusion data with Tract-Based Spatial Statistics

    Stephen M Smith;Heidi Johansen-Berg;Mark Jenkinson;Daniel Rueckert

  • Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction

    Chen Qin;Jo Schlemper;Jose Caballero;Anthony N. Price

  • Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration

    D. Rueckert;A.F. Frangi;J.A. Schnabel

  • The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction

    Antonios Makropoulos;Emma C. Robinson;Emma C. Robinson;Andreas Schuh;Robert Wright

  • Self-supervised learning for medical image analysis using image context restoration.

    Liang Chen;Paul Bentley;Kensaku Mori;Kazunari Misawa

  • Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling

    A.F. Frangi;D. Rueckert;J.A. Schnabel;W.J. Niessen

  • Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

Frequent Co-Authors

Joseph V. Hajnal
Joseph V. Hajnal King's College London
Wenjia Bai
Wenjia Bai Imperial College London
Paul Aljabar
Paul Aljabar King's College London
Ben Glocker
Ben Glocker Imperial College London
Bernhard Kainz
Bernhard Kainz Imperial College London
Wenzhe Shi
Wenzhe Shi Twitter (United States)
Christian Ledig
Christian Ledig University of Bamberg
Ozan Oktay
Ozan Oktay Imperial College London
Alexander Hammers
Alexander Hammers King's College London
Konstantinos Kamnitsas
Konstantinos Kamnitsas University of Oxford

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