World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
60
Citations
24281
World Ranking
3167
National Ranking
187

Tom Vercauteren publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Tom Vercauteren sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 307 publications — 75th percentile

75% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Tom Vercauteren D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Tom Vercauteren sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 60 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Tom Vercauteren is affiliated with King's College London in the United Kingdom. Their work spans several fields of study, primarily Medicine, Computer Science, and Engineering. Within these areas, their research focuses on subfields such as Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Biomedical Engineering, Artificial Intelligence, and Pediatrics, Perinatology and Child Health.

The scientist has contributed extensively to topics including Medical Image Segmentation Techniques, Fetal and Pediatric Neurological Disorders, Photoacoustic and Ultrasonic Imaging, Radiomics and Machine Learning in Medical Imaging, Advanced Neural Network Applications, Domain Adaptation and Few-Shot Learning, and Optical Imaging and Spectroscopy Techniques.

Among recent publications are:

  • MONAI: An open-source framework for deep learning in healthcare (2022, arXiv (Cornell University))
  • MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning (2021, Medical Image Analysis)
  • Artificial intelligence and medical education: A global mixed-methods study of medical students' perspectives (2022, Digital Health)
  • Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm (2021, Scientific Data)
  • MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images (2024, Medical Image Analysis)

Frequent co-authors working with Tom Vercauteren include:

  • Sébastien Ourselin
  • Jonathan Shapey
  • Jan Deprest
  • Lucas Fidon
  • Reuben Dorent

The scientist's publications appear regularly in venues such as:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • International Journal of Computer Assisted Radiology and Surgery
  • Medical Image Analysis
  • bioRxiv (Cold Spring Harbor Laboratory)

Best Publications

  • 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

  • Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

    Carole H. Sudre;Carole H. Sudre;Wenqi Li;Tom Vercauteren;Sebastien Ourselin;Sebastien Ourselin

  • Diffeomorphic demons: efficient non-parametric image registration.

    Tom Vercauteren;Xavier Pennec;Aymeric Perchant;Nicholas Ayache

  • 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

  • CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

    Ran Gu;Guotai Wang;Tao Song;Rui Huang

  • Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning

    Guotai Wang;Wenqi Li;Maria A. Zuluaga;Rosalind Pratt

  • Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks.

    Guotai Wang;Guotai Wang;Guotai Wang;Wenqi Li;Wenqi Li;Michael Aertsen;Jan Deprest

  • NiftyNet: a deep-learning platform for medical imaging

    Eli Gibson;Wenqi Li;Carole H. Sudre;Lucas Fidon

  • Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

    Guotai Wang;Wenqi Li;Sébastien Ourselin;Tom Vercauteren

  • Non-parametric diffeomorphic image registration with the demons algorithm

    Tom Vercauteren;Xavier Pennec;Aymeric Perchant;Nicholas Ayache

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

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

  • Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach

    Tom Vercauteren;Xavier Pennec;Aymeric Perchant;Nicholas Ayache

  • DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

    Guotai Wang;Maria A. Zuluaga;Wenqi Li;Rosalind Pratt

  • Weakly-supervised convolutional neural networks for multimodal image registration.

    Yipeng Hu;Yipeng Hu;Marc Modat;Eli Gibson;Wenqi Li

  • Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration

    B.T.T. Yeo;M.R. Sabuncu;T. Vercauteren;N. Ayache

  • On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

    Wenqi Li;Guotai Wang;Lucas Fidon;Sebastien Ourselin

  • An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI.

    Michael Ebner;Michael Ebner;Guotai Wang;Guotai Wang;Guotai Wang;Wenqi Li;Wenqi Li;Michael Aertsen

  • Automatic Brain Tumor Segmentation Based on Cascaded Convolutional Neural Networks With Uncertainty Estimation.

    Guotai Wang;Wenqi Li;Sébastien Ourselin;Tom Vercauteren

  • Decentralized sigma-point information filters for target tracking in collaborative sensor networks

    T. Vercauteren;Xiaodong Wang

  • Robust mosaicing with correction of motion distortions and tissue deformations for in vivo fibered microscopy.

    Tom Vercauteren;Aymeric Perchant;Grégoire Malandain;Xavier Pennec

Frequent Co-Authors

Sebastien Ourselin
Sebastien Ourselin King's College London
Jan Deprest
Jan Deprest KU Leuven
Danail Stoyanov
Danail Stoyanov University College London
Nicholas Ayache
Nicholas Ayache French Institute for Research in Computer Science and Automation - INRIA
Marc Modat
Marc Modat King's College London
Adrien E. Desjardins
Adrien E. Desjardins University College London
Dean C. Barratt
Dean C. Barratt University College London
David Atkinson
David Atkinson University of Liverpool
Xavier Pennec
Xavier Pennec French Institute for Research in Computer Science and Automation - INRIA

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