World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
79
Citations
48220
World Ranking
1114
National Ranking
595

Medicine

D-Index
83
Citations
35450
World Ranking
15397
National Ranking
7765

Milan Sonka publications per year

1986: 1 publications 1987: 0 publications 1988: 0 publications 1989: 1 publications 1990: 0 publications 1991: 1 publications 1992: 2 publications 1993: 14 publications 1994: 6 publications 1995: 7 publications 1996: 13 publications 1997: 15 publications 1998: 22 publications 1999: 12 publications 2000: 13 publications 2001: 21 publications 2002: 25 publications 2003: 27 publications 2004: 27 publications 2005: 32 publications 2006: 29 publications 2007: 16 publications 2008: 16 publications 2009: 26 publications 2010: 32 publications 2011: 27 publications 2012: 29 publications 2013: 34 publications 2014: 23 publications 2015: 27 publications 2016: 30 publications 2017: 23 publications 2018: 9 publications 2019: 9 publications 2020: 19 publications 2021: 16 publications 2022: 10 publications 2023: 30 publications 2024: 18 publications 2025: 13 publications 2026: 1 publications
1986 2026

676 publications in total across all disciplines

Milan Sonka publication distribution in Computer Science in 2027

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2027. The highlighted bar marks where Milan Sonka 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: 508 publications — 93rd percentile

93% of scientists in this discipline score the same or lower.

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

Milan Sonka D-index placement in Computer Science in 2027

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2027. The highlighted bar marks where Milan Sonka 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: 79 D-Index — 92nd percentile

92% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2006 - Fellow of the Indian National Academy of Engineering (INAE)
  • 2002 - IEEE Fellow For contributions to medical image analysis and computer vision.

Overview

Milan Sonka is affiliated with the University of Iowa in the United States. Their research focuses primarily on the intersection of medicine and computer science, with a significant emphasis on medical image analysis and its applications.

The scientist's work spans two main fields of study:

  • Medicine
  • Computer Science

Within these fields, the subfields of study include:

  • Radiology, Nuclear Medicine and Imaging
  • Computer Vision and Pattern Recognition
  • Surgery
  • Artificial Intelligence
  • Biomedical Engineering

The main topics addressed in Milan Sonka's research involve:

  • Radiomics and Machine Learning in Medical Imaging
  • Transplantation: Methods and Outcomes
  • AI in cancer detection
  • Advanced Neural Network Applications
  • COVID-19 diagnosis using AI
  • Glioma Diagnosis and Treatment
  • Coronary Interventions and Diagnostics

Milan Sonka has published extensively across various venues, with frequent publications in:

  • arXiv (Cornell University)
  • Medical Image Analysis
  • The Journal of Heart and Lung Transplantation
  • Journal of the American College of Cardiology
  • Journal of Neuroscience Methods

Notable recent papers include:

  • COVID TV-Unet: Segmenting COVID-19 chest CT images using connectivity imposed Unet, 2021, Informationszentrum Lebenswissenschaften (ZB MED)
  • Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning, 2020, Medical Image Analysis
  • Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques, 2021, European Radiology
  • U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation, 2023, arXiv (Cornell University)

Coauthors frequently collaborating with Milan Sonka include:

  • Andreas Wahle (14 publications)
  • Honghai Zhang (12 publications)
  • Girish Bathla (11 publications)
  • Zhi Chen (11 publications)
  • Sean Mullan (10 publications)

In addition to journal and conference papers, Milan Sonka has contributed to book publications, including:

  • Medical Image Analysis - Teaser to First Edition, 2023, published by European Organization for Nuclear Research

Milan Sonka has received professional recognition including:

  • Fellow of the Indian National Academy of Engineering (INAE), 2006
  • IEEE Fellow, 2002, for contributions to medical image analysis and computer vision

Best Publications

  • Image Processing: Analysis and Machine Vision

    Milan Sonka;Vaclav Hlavac;Roger Boyle

  • 3D Slicer as an image computing platform for the Quantitative Imaging Network.

    Andriy Fedorov;Reinhard Beichel;Jayashree Kalpathy-Cramer;Julien Finet

  • Retinal Imaging and Image Analysis

    M D Abràmoff;M K Garvin;M Sonka

  • Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning.

    Shervin Minaee;Rahele Kafieh;Milan Sonka;Shakib Yazdani

  • Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach

    Kang Li;Xiaodong Wu;D.Z. Chen;M. Sonka

  • Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images

    M.K. Garvin;M.D. Abramoff;Xiaodong Wu;S.R. Russell

  • Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus.

    Elliott H. Sohn;Hille W. van Dijk;Chunhua Jiao;Pauline H. B. Kok

  • 3-D active appearance models: segmentation of cardiac MR and ultrasound images

    S.C. Mitchell;J.G. Bosch;B.P.F. Lelieveldt;R.J. van der Geest

  • Effect of endothelial shear stress on the progression of coronary artery disease, vascular remodeling, and in-stent restenosis in humans: in vivo 6-month follow-up study.

    Peter H. Stone;Ahmet U. Coskun;Scott Kinlay;Maureen E. Clark

  • Image processing analysis and machine vision [2nd ed.]

    Milan Sonka;Václav Hlaváč;Roger Boyle

  • Interstitial lung disease: A quantitative study using the adaptive multiple feature method.

    Renuka Uppaluri;Eric A. Hoffman;Milan Sonka;Gary W. Hunninghake

  • Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images

    S.C. Mitchell;B.P.F. Lelieveldt;R.J. van der Geest;H.G. Bosch

  • Selective Loss of Inner Retinal Layer Thickness in Type 1 Diabetic Patients with Minimal Diabetic Retinopathy

    Hille W. van Dijk;Pauline H. B. Kok;Mona Garvin;Milan Sonka

  • Decreased retinal ganglion cell layer thickness in patients with type 1 diabetes.

    Hille W. van Dijk;Frank D. Verbraak;Pauline H. B. Kok;Mona K. Garvin

  • Early Neurodegeneration in the Retina of Type 2 Diabetic Patients

    Hille W. van Dijk;Frank D. Verbraak;Pauline H. B. Kok;Marilette Stehouwer

  • Intraretinal Layer Segmentation of Macular Optical Coherence Tomography Images Using Optimal 3-D Graph Search

    M.K. Garvin;M.D. Abramoff;R. Kardon;S.R. Russell

  • Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans

    J. Tschirren;E.A. Hoffman;G. McLennan;M. Sonka

  • Automatic segmentation of echocardiographic sequences by active appearance motion models

    J.G. Bosch;S.C. Mitchell;B.P.F. Lelieveldt;F. Nijland

  • Quantification of Pulmonary Emphysema from Lung Computed Tomography Images

    Renuka Uppaluri;Theophano Mitsa;Milan Sonka;Eric A. Hoffman

  • A Fully Parallel 3D Thinning Algorithm and Its Applications

    C.Min Ma;Milan Sonka

Frequent Co-Authors

Michael D. Abràmoff
Michael D. Abràmoff University of Iowa
Eric A. Hoffman
Eric A. Hoffman University of Iowa
Meindert Niemeijer
Meindert Niemeijer Digital Diagnostics Inc.
Boudewijn P. F. Lelieveldt
Boudewijn P. F. Lelieveldt Leiden University Medical Center
Johan H. C. Reiber
Johan H. C. Reiber Leiden University Medical Center
Joseph M. Reinhardt
Joseph M. Reinhardt University of Iowa
Randy H. Kardon
Randy H. Kardon University of Iowa
John M. Buatti
John M. Buatti University of Iowa
Aleš Linhart
Aleš Linhart Charles University
Hossein Rabbani
Hossein Rabbani Isfahan University of Medical Sciences

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