World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
5219
World Ranking
13051
National Ranking
832

Martin Rajchl 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 Martin Rajchl 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: 107 publications — 11th percentile

11% of scientists in this discipline score the same or lower.

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

Martin Rajchl 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 Martin Rajchl 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: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Overview

Martin Rajchl is affiliated with Imperial College London in the United Kingdom. Their research primarily focuses on the field of Medicine, with specific studies in Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Genetics, and Cancer Research.

Their work covers several main research topics, including:

  • Radiomics and Machine Learning in Medical Imaging
  • Advanced MRI Techniques and Applications
  • Cardiac Imaging and Diagnostics
  • Advanced X-ray and CT Imaging
  • Glioma Diagnosis and Treatment
  • Cancer Genomics and Diagnostics

Martin Rajchl has contributed to several recent publications, addressing different aspects of medical imaging and cancer research. Notable papers include:

  • Fully automated segmentation of left ventricular scar from 3D late gadolinium enhancement magnetic resonance imaging using a cascaded multi-planar U-Net (CMPU-Net), 2020, Medical Physics
  • Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma, 2024, GigaScience
  • EPCO-15. LINKING HISTOLOGICAL GLIOBLASTOMA PHENOTYPES TO TRANSCRIPTIONAL SUBTYPES AND PROGNOSIS USING DEEP LEARNING, 2022, Neuro-Oncology

Throughout their research career, Rajchl has frequently collaborated with several coauthors. Frequent coauthors include:

  • Thomas Roetzer-Pejrimovsky
  • Karl-Heinz Nenning
  • Barbara Kiesel
  • Johanna Klughammer
  • Bernhard Baumann

Their work has appeared in multiple publication venues, reflecting their interdisciplinary approach in medical imaging and cancer research. Key venues of publication are:

  • Medical Physics
  • GigaScience
  • Neuro-Oncology

Rajchl's research integrates advanced machine learning techniques to address challenges in medical image segmentation, glioma diagnosis, and linking histological phenotypes to transcriptional subtypes. Their studies contribute to understanding and improving diagnostic accuracy and prognostic evaluation in oncology and cardiology.

Best Publications

  • 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

  • Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

    Wenjia Bai;Matthew Sinclair;Giacomo Tarroni;Ozan Oktay

  • Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

    Konstantinos Kamnitsas;Wenjia Bai;Enzo Ferrante;Steven G. McDonagh

  • DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

    Martin Rajchl;Matthew C. H. Lee;Ozan Oktay;Konstantinos Kamnitsas

  • Semi-supervised learning for network-based cardiac MR image segmentation

    Wenjia Bai;Ozan Oktay;Matthew Sinclair;Hideaki Suzuki

  • Metric learning with spectral graph convolutions on brain connectivity networks.

    Sofia Ira Ktena;Sarah Parisot;Enzo Ferrante;Martin Rajchl

  • MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans

    Adriënne M. Mendrik;Koen L. Vincken;Hugo J. Kuijf;Marcel Breeuwer

  • Right ventricle segmentation from cardiac MRI: a collation study.

    Caroline Petitjean;Maria A. Zuluaga;Wenjia Bai;Jean Nicolas Dacher

  • Distance metric learning using graph convolutional networks: application to functional brain networks

    Sofia Ira Ktena;Sarah Parisot;Enzo Ferrante;Martin Rajchl

  • Multi-modal Learning from Unpaired Images: Application to Multi-organ Segmentation in CT and MRI

    Vanya V. Valindria;Nick Pawlowski;Martin Rajchl;Ioannis Lavdas

  • Prostate Segmentation: An Efficient Convex Optimization Approach With Axial Symmetry Using 3-D TRUS and MR Images

    Wu Qiu;Jing Yuan;Eranga Ukwatta;Yue Sun

  • Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders

    Nick Pawlowski;Matthew C.H. Lee;Martin Rajchl;Steven McDonagh

  • Implicit Weight Uncertainty in Neural Networks.

    Nick Pawlowski;Martin Rajchl;Ben Glocker

  • DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

    Nick Pawlowski;Sofia Ira Ktena;Matthew C. H. Lee;Bernhard Kainz

  • Left ventricle segmentation in MRI via convex relaxed distribution matching

    Cyrus M.S. Nambakhsh;Jing Yuan;Kumaradevan Punithakumar;Aashish Goela

  • Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images

    Ozan Oktay;Wenjia Bai;Ricardo Guerrero;Martin Rajchl

  • Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI

    Amir Alansary;Konstantinos Kamnitsas;Alice Davidson;Rostislav Khlebnikov

  • Interactive Hierarchical-Flow Segmentation of Scar Tissue From Late-Enhancement Cardiac MR Images

    Martin Rajchl;Jing Yuan;James A. White;Eranga Ukwatta

  • Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

    Konstantinos Kamnitsas;Wenjia Bai;Enzo Ferrante;Steven McDonagh

  • Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling

    Carlo Biffi;Ozan Oktay;Giacomo Tarroni;Wenjia Bai

  • Dual optimization based prostate zonal segmentation in 3D MR images.

    Wu Qiu;Jing Yuan;Eranga Ukwatta;Yue Sun

  • 3-D Carotid Multi-Region MRI Segmentation by Globally Optimal Evolution of Coupled Surfaces

    E. Ukwatta;Jing Yuan;M. Rajchl;Wu Qiu

Frequent Co-Authors

Terry M. Peters
Terry M. Peters University of Western Ontario
Daniel Rueckert
Daniel Rueckert Technical University of Munich
Aaron Fenster
Aaron Fenster University of Western Ontario
Ben Glocker
Ben Glocker Imperial College London
Wenjia Bai
Wenjia Bai Imperial College London
Ozan Oktay
Ozan Oktay Imperial College London
Bernhard Kainz
Bernhard Kainz Imperial College London
Konstantinos Kamnitsas
Konstantinos Kamnitsas University of Oxford
Paul M. Matthews
Paul M. Matthews Imperial College London
Joseph V. Hajnal
Joseph V. Hajnal King's College London

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens doors to a variety of related online degree options and career paths. Many students interested in computer science also explore fields like mechanical or electrical engineering, physics, and data science. There are now affordable, flexible programs available online, making these options more accessible than ever for international and domestic students alike.

For those considering a broader engineering background, the cheapest mechanical engineering degree online can offer foundational skills highly valued in tech and software industries. Similarly, a bachelor of science in physics online is a great pathway for students interested in problem-solving, data analysis, and scientific computing.

Data Science is another fast-growing field with excellent job prospects. Choosing the cheapest data science degree can provide specialized skills at a lower cost. If you’re interested in the intersection of software and hardware, consider the online electrical engineering career outcomes to see how this degree can lead to high-demand roles in tech and innovation.

Best Scientists Citing Martin Rajchl

Trending Scientists

Recently Published Articles