World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
12147
World Ranking
4800
National Ranking
286

Paul Aljabar 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 Paul Aljabar 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: 154 publications — 28th percentile

28% of scientists in this discipline score the same or lower.

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

Paul Aljabar 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 Paul Aljabar 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: 53 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Overview

Paul Aljabar is affiliated with King's College London in the United Kingdom, where their research activity spans multiple disciplines within medicine and computer science. Their work focuses particularly on radiology, nuclear medicine, and imaging, with additional engagement in epidemiology, cardiology and cardiovascular medicine, oncology, and biomedical engineering.

Their scientific interests cover key topics such as liver disease diagnosis and treatment, radiomics and machine learning applications in medical imaging, pancreatic and hepatic oncology research, COVID-19 diagnosis using artificial intelligence, advanced MRI techniques and applications, advanced X-ray and CT imaging, as well as cardiovascular disease and adiposity.

Paul Aljabar has contributed to numerous peer-reviewed publications, including:

  • Pancreas MRI Segmentation Into Head, Body, and Tail Enables Regional Quantitative Analysis of Heterogeneous Disease, 2022, Journal of Magnetic Resonance Imaging
  • Pancreas MRI segmentation into head, body, and tail enables regional quantitative analysis of heterogeneous disease, 2021, bioRxiv (Cold Spring Harbor Laboratory)
  • Quantitative digital pathology enables automated and quantitative assessment of inflammatory activity in patients with autoimmune hepatitis, 2024, Journal of Pathology Informatics
  • Su1944: AI MODELS FOR QC AND NON-MUCOSAL FEATURE DETECTION IN HISTOLOGICAL PINCH BIOPSIES OF INFLAMMATORY BOWEL DISEASE, 2025, Gastroenterology
  • Estimation of field inhomogeneity map following magnitude-based ambiguity-resolved water-fat separation, 2023, Magnetic Resonance Imaging

Their frequent coauthors include Andrew P. King, Alexandre Triay Bagur, Caitlin Langford, Dylan Windell, and Robert Goldin.

Paul Aljabar's research has appeared repeatedly in prominent venues including Gastroenterology, the Proceedings of the International Society for Magnetic Resonance in Medicine Scientific Meeting and Exhibition, bioRxiv, Radiotherapy and Oncology, and arXiv.

Best Publications

  • 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

  • Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease

    Katherine R. Gray;Paul Aljabar;Rolf A. Heckemann;Alexander Hammers

  • Diffeomorphic registration using b-splines

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

  • Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain

    Antonios Makropoulos;Ioannis S Gousias;Christian Ledig;Paul Aljabar

  • 3-D In Vitro Acoustic Super-Resolution and Super-Resolved Velocity Mapping Using Microbubbles

    Kirsten Christensen-Jeffries;Jemma Brown;Paul Aljabar;Mengxing Tang

  • Rich-club organization of the newborn human brain

    Gareth Ball;Paul Aljabar;Sally Zebari;Nora Tusor

  • Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression

    Ahmed Serag;Paul Aljabar;Gareth Ball;Serena J. Counsell

  • Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer

    Tim Lustberg;Johan van Soest;Mark Gooding;Devis Peressutti

  • A dynamic 4D probabilistic atlas of the developing brain

    Maria Kuklisova-Murgasova;Paul Aljabar;Latha Srinivasan;Serena J. Counsell

  • LEAP: Learning embeddings for atlas propagation

    Robin Wolz;Paul Aljabar;Joseph V. Hajnal;Alexander Hammers

  • Abnormal deep grey matter development following preterm birth detected using deformation-based morphometry

    James P. Boardman;Serena J. Counsell;Daniel Rueckert;Olga Kapellou

  • Early development of structural networks and the impact of prematurity on brain connectivity

    Dafnis Batalle;Emer J. Hughes;Hui Zhang;J.-Donald Tournier

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

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

  • Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017.

    Jinzhong Yang;Harini Veeraraghavan;Samuel G. Armato;Keyvan Farahani

  • Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation.

    Rolf A. Heckemann;Shiva Keihaninejad;Paul Aljabar;Daniel Rueckert

  • Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease.

    Katherine R. Gray;Robin Wolz;Rolf A. Heckemann;Paul Aljabar

  • Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring

    Lisanne V van Dijk;Lisa Van den Bosch;Paul Aljabar;Devis Peressutti

  • Regional growth and atlasing of the developing human brain

    Antonios Makropoulos;Paul Aljabar;Robert Wright;Britta Hüning

  • Fast Volume Reconstruction From Motion Corrupted Stacks of 2D Slices

    Bernhard Kainz;Markus Steinberger;Wolfgang Wein;Maria Kuklisova-Murgasova

  • A common neonatal image phenotype predicts adverse neurodevelopmental outcome in children born preterm

    J P Boardman;C Craven;S Valappil;S J Counsell

Frequent Co-Authors

Daniel Rueckert
Daniel Rueckert Technical University of Munich
Joseph V. Hajnal
Joseph V. Hajnal King's College London
Serena J. Counsell
Serena J. Counsell King's College London
A. David Edwards
A. David Edwards King's College London
Mary A. Rutherford
Mary A. Rutherford King's College London
Alexander Hammers
Alexander Hammers King's College London
Andrew P. King
Andrew P. King King's College London
Christian Ledig
Christian Ledig University of Bamberg
Julia A. Schnabel
Julia A. Schnabel King's College London
Meng-Xing Tang
Meng-Xing Tang Imperial College London

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