World's Best Scientists 2026 revealed!
Seung-Koo Lee

Seung-Koo Lee

D-Index & Metrics

Neuroscience

D-Index
49
Citations
9691
World Ranking
5949
National Ranking
30

Seung-Koo Lee publication distribution in Neuroscience in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Neuroscience in 2026. The highlighted bar marks where Seung-Koo Lee sits on this spectrum.

38–47 publications: 18 scientists 48–57 publications: 79 scientists 58–67 publications: 193 scientists 68–77 publications: 323 scientists 78–87 publications: 406 scientists 88–97 publications: 452 scientists 98–107 publications: 539 scientists 108–117 publications: 505 scientists 118–127 publications: 522 scientists 128–137 publications: 469 scientists 138–147 publications: 456 scientists 148–157 publications: 459 scientists 158–167 publications: 397 scientists 168–177 publications: 383 scientists 178–187 publications: 350 scientists 188–197 publications: 302 scientists 198–207 publications: 306 scientists 208–217 publications: 262 scientists 218–227 publications: 242 scientists 228–237 publications: 220 scientists 238–247 publications: 203 scientists 248–257 publications: 174 scientists 258–267 publications: 176 scientists 268–277 publications: 175 scientists 278–287 publications: 125 scientists 288–297 publications: 116 scientists 298–307 publications: 127 scientists 308–317 publications: 128 scientists 318–327 publications: 99 scientists 328–337 publications: 89 scientists 338–347 publications: 78 scientists 348–357 publications: 96 scientists 358–367 publications: 66 scientists 368–377 publications: 59 scientists 378–387 publications: 65 scientists 388–397 publications: 54 scientists 398–407 publications: 48 scientists 408–417 publications: 49 scientists 418–427 publications: 34 scientists 428–437 publications: 31 scientists 438–447 publications: 30 scientists 448–457 publications: 31 scientists 458–467 publications: 36 scientists 468–477 publications: 40 scientists 478–487 publications: 35 scientists 488–497 publications: 30 scientists 498–507 publications: 23 scientists 508–517 publications: 26 scientists 518–527 publications: 20 scientists 528–537 publications: 23 scientists 538–547 publications: 20 scientists 548–557 publications: 20 scientists 558–567 publications: 17 scientists 568–577 publications: 14 scientists 578–587 publications: 20 scientists 588–597 publications: 20 scientists 598–607 publications: 19 scientists 608–617 publications: 18 scientists 618–627 publications: 17 scientists 628–637 publications: 11 scientists 638–647 publications: 11 scientists 648–657 publications: 11 scientists 658–667 publications: 8 scientists 668–677 publications: 7 scientists 678–687 publications: 11 scientists 688–697 publications: 10 scientists 698–707 publications: 4 scientists 708–717 publications: 6 scientists 718–727 publications: 5 scientists 728–737 publications: 5 scientists 738–747 publications: 9 scientists 748–757 publications: 9 scientists 758–767 publications: 3 scientists 768–777 publications: 7 scientists 778–787 publications: 7 scientists 788–797 publications: 6 scientists 798–807 publications: 2 scientists 808–817 publications: 2 scientists 818–827 publications: 7 scientists 828–837 publications: 0 scientists 838–847 publications: 9 scientists 848–857 publications: 3 scientists 858–867 publications: 1 scientists 868–877 publications: 3 scientists 878–886 publications: 6 scientists 887+ publications: 100 scientists
38 publications 887+

This scientist: 235 publications — 71st percentile

71% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 887 publications or more.

Seung-Koo Lee D-index placement in Neuroscience in 2026

The chart shows the D-index (discipline H-index) distribution of Neuroscience scientists ranked by Research.com in 2026. The highlighted bar marks where Seung-Koo Lee sits on this spectrum.

30–31 D-Index: 42 scientists 32–33 D-Index: 172 scientists 34–35 D-Index: 296 scientists 36–37 D-Index: 435 scientists 38–39 D-Index: 459 scientists 40–41 D-Index: 456 scientists 42–43 D-Index: 467 scientists 44–45 D-Index: 478 scientists 46–47 D-Index: 512 scientists 48–49 D-Index: 435 scientists 50–51 D-Index: 425 scientists 52–53 D-Index: 418 scientists 54–55 D-Index: 392 scientists 56–57 D-Index: 357 scientists 58–59 D-Index: 334 scientists 60–61 D-Index: 328 scientists 62–63 D-Index: 260 scientists 64–65 D-Index: 278 scientists 66–67 D-Index: 239 scientists 68–69 D-Index: 250 scientists 70–71 D-Index: 210 scientists 72–73 D-Index: 200 scientists 74–75 D-Index: 189 scientists 76–77 D-Index: 170 scientists 78–79 D-Index: 146 scientists 80–81 D-Index: 113 scientists 82–83 D-Index: 126 scientists 84–85 D-Index: 100 scientists 86–87 D-Index: 84 scientists 88–89 D-Index: 99 scientists 90–91 D-Index: 84 scientists 92–93 D-Index: 85 scientists 94–95 D-Index: 72 scientists 96–97 D-Index: 76 scientists 98–99 D-Index: 45 scientists 100–101 D-Index: 49 scientists 102–103 D-Index: 43 scientists 104–105 D-Index: 32 scientists 106–107 D-Index: 45 scientists 108–109 D-Index: 50 scientists 110–111 D-Index: 32 scientists 112–113 D-Index: 39 scientists 114–115 D-Index: 32 scientists 116–117 D-Index: 29 scientists 118–119 D-Index: 27 scientists 120–121 D-Index: 19 scientists 122–123 D-Index: 23 scientists 124–125 D-Index: 27 scientists 126–127 D-Index: 16 scientists 128–129 D-Index: 24 scientists 130–131 D-Index: 13 scientists 132–133 D-Index: 21 scientists 134–135 D-Index: 17 scientists 136–137 D-Index: 14 scientists 138–139 D-Index: 15 scientists 140–141 D-Index: 10 scientists 142–143 D-Index: 10 scientists 144–145 D-Index: 13 scientists 146–147 D-Index: 9 scientists 148–149 D-Index: 8 scientists 150–151 D-Index: 6 scientists 152–153 D-Index: 6 scientists 154–155 D-Index: 7 scientists 156–157 D-Index: 7 scientists 158–159 D-Index: 10 scientists 160–161 D-Index: 4 scientists 162 D-Index: 8 scientists 163+ D-Index: 100 scientists
30 D-Index 163+

This scientist: 49 D-Index — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 163 D-Index or more.

Overview

Seung-Koo Lee is affiliated with Yonsei University in South Korea and has contributed extensively to the field of medical research, particularly in radiology and medical imaging. Their work emphasizes the application of radiomics and machine learning techniques to improve diagnostic and prognostic processes in neurological and oncological diseases.

Their research covers a broad array of topics including:

  • Radiomics and Machine Learning in Medical Imaging
  • Glioma Diagnosis and Treatment
  • MRI in cancer diagnosis
  • Meningioma and schwannoma management
  • Brain Metastases and Treatment
  • Advanced Neuroimaging Techniques and Applications
  • Medical Imaging Techniques and Applications

Seung-Koo Lee's main field of study is Medicine, with 387 published works. Their subfields include:

  • Radiology, Nuclear Medicine and Imaging
  • Genetics
  • Neurology
  • Pulmonary and Respiratory Medicine
  • Epidemiology

Frequent publication venues for Lee's work are:

  • European Radiology
  • Neuro-Oncology
  • Korean Journal of Radiology
  • Neuroradiology
  • Yonsei Medical Journal

Selected notable papers include:

  • Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics (2020, Neuro-Oncology)
  • Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation (2020, Scientific Reports)
  • A deep learning algorithm may automate intracranial aneurysm detection on MR angiography with high diagnostic performance (2020, European Radiology)
  • Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction (2020, European Radiology)
  • Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results (2021, PLoS ONE)

Frequent co-authors collaborating with Lee include:

  • Sung Soo Ahn
  • Yae Won Park
  • Jong Hee Chang
  • Se Hoon Kim
  • Kyunghwa Han

Best Publications

  • Pediatric diffusion tensor imaging: normal database and observation of the white matter maturation in early childhood.

    Laurent Hermoye;Christine Saint-Martin;Guy Cosnard;Seung Koo Lee

  • Diffusion-tensor MR imaging and tractography : Exploring brain microstructure and connectivity

    Paolo G. P. Nucifora;Ragini Verma;Seung-Koo Lee;Elias R. Melhem

  • Free radicals as triggers of brain edema formation after stroke.

    Ji Hoe Heo;Sang Won Han;Seung Koo Lee

  • Diffusion-Tensor MR Imaging and Fiber Tractography: A New Method of Describing Aberrant Fiber Connections in Developmental CNS Anomalies

    Seung-Koo Lee;Dong Ik Kim;Jinna Kim;Dong Joon Kim

  • Corpus callosal connection mapping using cortical gray matter parcellation and DT-MRI

    Hae-Jeong Park;Jae Jin Kim;Seung-Koo Lee;Jeong Ho Seok

  • Predictors of surgical outcome and pathologic considerations in focal cortical dysplasia.

    D. W. Kim;S. K. Lee;K. Chu;K. I. Park

  • White matter abnormalities associated with auditory hallucinations in schizophrenia: a combined study of voxel-based analyses of diffusion tensor imaging and structural magnetic resonance imaging.

    Jeong Ho Seok;Jeong Ho Seok;Hae Jeong Park;Ji Won Chun;Seung Koo Lee

  • Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics

    Yoon Seong Choi;Yoon Seong Choi;Yoon Seong Choi;Sohi Bae;Jong Hee Chang;Seok Gu Kang

  • Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection

    Andreia V. Faria;Jiangyang Zhang;Kenichi Oishi;Xin Li

  • Results of Transvenous Embolization of Cavernous Dural Arteriovenous Fistula: A Single-Center Experience with Emphasis on Complications and Management

    D J Kim;D I Kim;S H Suh;J Kim

  • Stent-assisted coil embolization of intracranial wide-necked aneurysms.

    Young-Jun Lee;Dong Joon Kim;Sang Hyun Suh;Seung-Koo Lee

  • Mitochondrial respiratory chain defects: Underlying etiology in various epileptic conditions

    Young Mock Lee;Hoon Chul Kang;Joon Soo Lee;Se Hoon Kim

  • Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging

    Yae Won Park;Yae Won Park;Jongmin Oh;Seng Chan You;Kyunghwa Han

  • Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of MR images

    Raginia Verma;Evangelia I. Zacharaki;Yangming Ou;Hongmin Cai

  • Prediction of IDH1-Mutation and 1p/19q-Codeletion Status Using Preoperative MR Imaging Phenotypes in Lower Grade Gliomas.

    Y.W. Park;K. Han;S.S. Ahn;S. Bae

  • ORBIT: A Multiresolution Framework for Deformable Registration of Brain Tumor Images

    E.I. Zacharaki;Dinggang Shen;Seung-Koo Lee;C. Davatzikos

  • MR Imaging of Neoplastic Central Nervous System Lesions: Review and Recommendations for Current Practice

    Marco Essig;N. Anzalone;S. E. Combs;A. Dörfler

  • Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach

    Hie Bum Suh;Yoon Seong Choi;Sohi Bae;Sung Soo Ahn

  • MR imaging in multiple sclerosis: review and recommendations for current practice.

    Karl Olof Lövblad;N. Anzalone;A. Dörfler;M. Essig

  • Amide proton transfer imaging to discriminate between low- and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume

    Yoon Seong Choi;Sung Soo Ahn;Seung Koo Lee;Jong Hee Chang

Frequent Co-Authors

Phil Hyu Lee
Phil Hyu Lee Yonsei University
Susumu Mori
Susumu Mori Johns Hopkins University School of Medicine
Byung In Lee
Byung In Lee Inje University
Christos Davatzikos
Christos Davatzikos University of Pennsylvania
Heung Dong Kim
Heung Dong Kim Yonsei University Health System
Young H. Sohn
Young H. Sohn Yonsei University
Hae-Jeong Park
Hae-Jeong Park Yonsei University
Peter C.M. van Zijl
Peter C.M. van Zijl Kennedy Krieger Institute
Sang Won Seo
Sang Won Seo Samsung Medical Center
Daniel S. Marcus
Daniel S. Marcus Washington University in St. Louis

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