World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
20335
World Ranking
9018
National Ranking
3830

Koen Van Leemput 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 Koen Van Leemput 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: 123 publications — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Koen Van Leemput 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 Koen Van Leemput 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: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Overview

Koen Van Leemput is affiliated with Harvard University in the United States. Their research spans multiple disciplines, primarily focusing on Medicine and Computer Science. Within these fields, their work addresses several subfields including Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, and Neurology.

The scholar's research covers key topics such as Radiomics and Machine Learning in Medical Imaging, Medical Image Segmentation Techniques, Glioma Diagnosis and Treatment, Medical Imaging and Analysis, Advanced Neural Network Applications, Advanced Neuroimaging Techniques and Applications, and Functional Brain Connectivity Studies.

Their recent publications illustrate this focus with papers appearing in prominent venues. Notable works include:

  • SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining (2023, Medical Image Analysis)
  • Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling (2020, NeuroImage)
  • A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis (2020, NeuroImage)
  • A Learning Strategy for Contrast-agnostic MRI Segmentation (2020, arXiv (Cornell University))
  • The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) (2023, arXiv (Cornell University))

Koen Van Leemput has collaborated frequently with several coauthors, with the highest number of joint publications occurring with:

  • Juan Eugenio Iglesias (30 publications)
  • Adrian V. Dalca (14 publications)
  • Jake Albrecht (14 publications)
  • Verena Chung (14 publications)
  • Zhifan Jiang (13 publications)

Their work has been published extensively in venues recognized for research in biomedical imaging and related fields including:

  • arXiv (Cornell University) with 19 publications
  • bioRxiv (Cold Spring Harbor Laboratory) with 5 publications
  • NeuroImage with 4 publications
  • The Journal of Machine Learning for Biomedical Imaging with 3 publications
  • Medical Image Analysis with 2 publications

In addition to journal articles, they have contributed to several book publications through Springer Science+Business Media. Titles include:

  • Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis (2020)
  • Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis (2021)
  • Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (2022)

Best Publications

  • The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    Bjoern H. Menze;Andras Jakab;Stefan Bauer;Jayashree Kalpathy-Cramer

  • The Multimodal Brain TumorImage Segmentation Benchmark (BRATS)

    Bjoern Menze;Mauricio Reyes;Koen Van Leemput;Nicole Porz

  • Automated model-based tissue classification of MR images of the brain

    K. Van Leemput;F. Maes;D. Vandermeulen;P. Suetens

  • A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI

    Juan Eugenio Iglesias;Jean C. Augustinack;Khoa Nguyen;Christopher M. Player

  • Automated model-based bias field correction of MR images of the brain

    K. Van Leemput;F. Maes;D. Vandermeulen;P. Suetens

  • Automated segmentation of multiple sclerosis lesions by model outlier detection

    K. Van Leemput;F. Maes;D. Vandermeulen;A. Colchester

  • A Generative Model for Image Segmentation Based on Label Fusion

    Mert R Sabuncu;B T Thomas Yeo;K Van Leemput;Bruce Fischl

  • Automated segmentation of hippocampal subfields from ultra‐high resolution in vivo MRI

    Koen Van Leemput;Koen Van Leemput;Akram Bakkour;Thomas Benner;Graham Wiggins

  • A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology.

    Juan Eugenio Iglesias;Ricardo Insausti;Garikoitz Lerma-Usabiaga;Martina Bocchetta

  • A unifying framework for partial volume segmentation of brain MR images

    K. Van Leemput;F. Maes;D. Vandermeulen;P. Suetens

  • Quantitative Comparison of 21 Protocols for Labeling Hippocampal Subfields and Parahippocampal Subregions in In Vivo MRI: Towards a Harmonized Segmentation Protocol

    Paul A. Yushkevich;Robert S C Amaral;Jean C. Augustinack;Andrew R. Bender

  • Automatic brain tumor segmentation by subject specific modification of atlas priors.

    Marcel Prastawa;Elizabeth Bullitt;Nathan Moon;Koen Van Leemput

  • Bayesian segmentation of brainstem structures in MRI.

    Juan Eugenio Iglesias;Koen Van Leemput;Priyanka Bhatt;Christen Casillas

  • A generative model for brain tumor segmentation in multi- modal images

    Bjoern H. Menze;Koen Van Leemput;Danial Lashkari;Marc-André Weber

  • Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling.

    Oula Puonti;Juan Eugenio Iglesias;Koen Van Leemput;Koen Van Leemput

  • Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling.

    Oula Puonti;Oula Puonti;Koen Van Leemput;Koen Van Leemput;Guilherme B Saturnino;Guilherme B Saturnino;Hartwig R Siebner;Hartwig R Siebner

  • Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases.

    Juan Eugenio Iglesias;Koen Van Leemput;Jean Augustinack;Ricardo Insausti

  • Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference

    Jana Lipkova;Panagiotis Angelikopoulos;Stephen Wu;Esther Alberts

  • Is Synthesizing MRI Contrast Useful for Inter-modality Analysis?

    Juan Eugenio Iglesias;Ender Konukoglu;Darko Zikic;Ben Glocker

  • Automatic brain and tumor segmentation

    Nathan Moon;Elizabeth Bullitt;Koen Van Leemput;Guido Gerig

  • Patch-based generation of a pseudo CT from conventional MRI sequences for MRI-only radiotherapy of the brain.

    Daniel Andreasen;Koen Van Leemput;Rasmus H. Hansen;Jon A. L. Andersen

  • Model-based brain and tumor segmentation

    N. Moon;E. Bullitt;K. van Leemput;G. Gerig

Frequent Co-Authors

Bruce Fischl
Bruce Fischl Harvard University
Frederik Maes
Frederik Maes KU Leuven
Bjoern H. Menze
Bjoern H. Menze University of Zurich
Paul Suetens
Paul Suetens KU Leuven
Lawrence L. Wald
Lawrence L. Wald Harvard University
Jean C. Augustinack
Jean C. Augustinack Harvard University
Nicholas Ayache
Nicholas Ayache French Institute for Research in Computer Science and Automation - INRIA
Douglas N. Greve
Douglas N. Greve Harvard University

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

Exploring computer science doesn’t have to follow a traditional four-year route. Many students begin with an associate degree online, which provides foundational skills and a flexible schedule. For those looking to accelerate their journey, there are options for cheap online degrees fast, helping you save both time and money while gaining essential credentials.

Worried about admission requirements? Don’t let a lower GPA hold you back—there are college that accepts low gpa applicants, where your motivation and unique experiences matter just as much as your transcripts.

Graduates of computer science and related fields enjoy diverse career pathways. For example, if you’re interested in sustainability, there are numerous jobs for environmental science majors that fuse technology and environmental impact. Explore your options—online education opens doors to multiple opportunities tailored to your skills and interests.

Best Scientists Citing Koen Van Leemput

Trending Scientists

Recently Published Articles