World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
57
Citations
12809
World Ranking
3848
National Ranking
234

Research.com Recognitions

  • 2021 - IEEE Fellow For contributions to medical image computing

Overview

Julia A. Schnabel is affiliated with King's College London in the United Kingdom. Their research spans predominantly the fields of Medicine and Computer Science, with significant contributions to Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, and Computer Vision and Pattern Recognition.

The scientist's work covers several specialized subfields including Cardiology and Cardiovascular Medicine, as well as Biomedical Engineering. Their research topics focus on advanced medical imaging methods and applications, such as Advanced MRI Techniques and Applications, Radiomics and Machine Learning in Medical Imaging, AI in cancer detection, and Medical Image Segmentation Techniques. They also engage in areas related to Cardiac Imaging and Diagnostics and Anomaly Detection Techniques and Applications.

Julia A. Schnabel has authored multiple papers published in notable venues. Representative recent publications include:

  • Deep Learning for PET Image Reconstruction, 2020, IEEE Transactions on Radiation and Plasma Medical Sciences
  • A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study, 2023, Cancer Cell
  • MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography, 2024, bioRxiv (Cold Spring Harbor Laboratory)
  • FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare, 2025, BMJ

Their frequent coauthors consist of:

  • Veronika A. Zimmer
  • Daniel Rueckert
  • Cosmin I. Bercea
  • Veronika Spieker
  • Kerstin Hammernik

Julia A. Schnabel publishes often in venues such as arXiv, the Proceedings of the International Society for Magnetic Resonance in Medicine's Scientific Meeting and Exhibition, Medical Image Analysis, bioRxiv (Cold Spring Harbor Laboratory), and the European Heart Journal - Cardiovascular Imaging.

In addition to journal articles, they have contributed to books published by Springer Science+Business Media, including Information Processing in Medical Imaging (2021) and Predictive Intelligence in Medicine (2021).

Among the awards received, Julia A. Schnabel was named an IEEE Fellow in 2021 for contributions to medical image computing.

Best Publications

  • MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

    Mattias P. Heinrich;Mark Jenkinson;Manav Bhushan;Tahreema Matin

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

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

  • 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

  • A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations

    Julia A. Schnabel;Daniel Rueckert;Marcel Quist;Jane M. Blackall

  • Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

    K. Murphy;B. van Ginneken;J. M. Reinhardt;S. Kabus

  • Reconstruction of fetal brain MRI with intensity matching and complete outlier removal.

    Maria Kuklisova-Murgasova;Gerardine Quaghebeur;Mary A. Rutherford;Joseph V. Hajnal

  • Validation of nonrigid image registration using finite-element methods: application to breast MR images

    J.A. Schnabel;C. Tanner;A.D. Castellano-Smith;A. Degenhard

  • MRF-Based Deformable Registration and Ventilation Estimation of Lung CT

    H. P. Heinrich;M. Jenkinson;M. Brady;J. A. Schnabel

  • Non-local shape descriptor: a new similarity metric for deformable multi-modal registration

    Mattias P. Heinrich;Mark Jenkinson;Manav Bhushan;Tahreema Matin

  • An evaluation of four automatic methods of segmenting the subcortical structures in the brain.

    Kolawole Oluwole Babalola;Brian Patenaude;Paul Aljabar;Julia A. Schnabel

  • Breast Image Analysis for Risk Assessment, Detection, Diagnosis, and Treatment of Cancer

    Maryellen L Giger;Nico Karssemeijer;Julia A Schnabel

  • Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration

    Daniel Rueckert;Alejandro F. Frangi;Alejandro F. Frangi;Julia A. Schnabel

  • A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology.

    James Clough;Nicholas Byrne;Ilkay Oksuz;Veronika A. Zimmer

  • Deep Learning for PET Image Reconstruction

    Andrew J. Reader;Guillaume Corda;Abolfazl Mehranian;Casper da Costa-Luis

  • Towards Realtime Multimodal Fusion for Image-Guided Interventions Using Self-similarities.

    Mattias Paul Heinrich;Mark Jenkinson;Bartlomiej W. Papież;Sir Michael Brady

  • Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging.

    Geoffrey J.M. Parker;Julia A. Schnabel;Mark R. Symms;David J. Werring

  • Objective assessment of deformable image registration in radiotherapy: A multi-institution study

    Rojano Kashani;Martina Hub;James M. Balter;Marc L. Kessler

  • Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function.

    Bram Ruijsink;Bram Ruijsink;Esther Puyol-Antón;Ilkay Oksuz;Matthew Sinclair

  • Left-Ventricle Quantification Using Residual U-Net

    Eric Kerfoot;James R. Clough;Ilkay Öksüz;Jack Lee

  • Factors influencing the accuracy of biomechanical breast models.

    Christine Tanner;Julia A. Schnabel;Derek L. G. Hill;David J. Hawkes

  • Registration-based interpolation

    G.P. Penney;J.A. Schnabel;D. Rueckert;M.A. Viergever

Frequent Co-Authors

Daniel Rueckert
Daniel Rueckert Technical University of Munich
David J. Hawkes
David J. Hawkes University College London
Andrew P. King
Andrew P. King King's College London
Mattias P. Heinrich
Mattias P. Heinrich University of Lübeck
Derek L. G. Hill
Derek L. G. Hill Panoramic Digital Health
Mark Jenkinson
Mark Jenkinson University of Oxford
Claudia Prieto
Claudia Prieto Pontificia Universidad Católica de Chile
Michael Brady
Michael Brady University of Oxford
Joseph V. Hajnal
Joseph V. Hajnal King's College London
Alejandro F. Frangi
Alejandro F. Frangi University of Manchester

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