litlas
Log inCreate account

Frederik Maes

Active 1990–2025

197
Papers
19,390
Citations
58
h-index
126
i10-index

Citations

Citations per year for Frederik Maes1884: 2 citations1951: 2 citations1965: 1 citations1969: 1 citations1971: 2 citations1973: 1 citations1974: 5 citations1989: 1 citations1990: 7 citations1991: 3 citations1995: 1 citations1996: 10 citations1997: 28 citations1998: 78 citations1999: 89 citations2000: 134 citations2001: 198 citations2002: 222 citations2003: 393 citations2004: 315 citations2005: 308 citations2006: 349 citations2007: 368 citations2008: 413 citations2009: 421 citations2010: 395 citations2011: 414 citations2012: 340 citations2013: 304 citations2014: 313 citations2015: 264 citations2016: 265 citations2017: 246 citations2018: 327 citations2019: 441 citations2020: 431 citations2021: 453 citations2022: 417 citations2023: 369 citations2024: 450 citations2025: 301 citations2026: 53 citations1885–1950: no citations, so these years are not shown1952–1964: no citations, so these years are not shown1966–1968: no citations, so these years are not shown1970: no citations, so this year is not shown1972: no citations, so this year is not shown1975–1988: no citations, so these years are not shown1992–1994: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 2,295 citing papers, 21.9% of this breakdownChina: 1,115 citing papers, 10.6% of this breakdownUnited Kingdom: 792 citing papers, 7.5% of this breakdownGermany: 651 citing papers, 6.2% of this breakdownFrance: 561 citing papers, 5.3% of this breakdownBelgium: 487 citing papers, 4.6% of this breakdownCanada: 450 citing papers, 4.3% of this breakdownNetherlands: 447 citing papers, 4.3% of this breakdownIndia: 300 citing papers, 2.9% of this breakdownSwitzerland: 263 citing papers, 2.5% of this breakdownItaly: 263 citing papers, 2.5% of this breakdownSpain: 253 citing papers, 2.4% of this breakdown
0%21.9%Other 25%

Institutions

Fields

  • Computer Science40.6%
  • Medicine31.2%
  • Engineering7.8%
  • Neuroscience7.5%
  • Dentistry4.1%
  • Biochemistry, Genetics and Molecular Biology3%
  • Other5.8%

Topics

  • Medical Image Segmentation Techniques14.5%
  • Medical Imaging Techniques and Applications4.7%
  • Advanced MRI Techniques and Applications3.4%
  • Radiomics and Machine Learning in Medical Imaging3.3%
  • Brain Tumor Detection and Classification3.1%
  • Advanced Neural Network Applications2.9%
  • Other68.1%

Coauthors

All papers

Open in search
  1. Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index

    Authors: Tom Eelbode, Jeroen Bertels, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops, Matthew B. Blaschko - IEEE Transactions on Medical Imaging, IEEE Trans. Medical Imaging 2020 cited by 414

  2. Multimodality Image Registration by Maximization of Mutual Information

    Authors: Frederik Maes, André Collignon, Dirk Vandermeulen, Guy Marchal, Paul Suetens - IEEE Transactions on Medical Imaging, IEEE Trans. Medical Imaging 1997 cited by 4,520

  3. ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

    Authors: Oskar Maier, Bjoern Menze, Janina von der Gablentz, Levin Häni, Mattias P. Heinrich‬, Matthias Liebrand, Stefan Winzeck, A. Basit, Paul Bentley, Liang Chen, Daan Christiaens, Francis Dutil, Karl Egger, Chaolu Feng, Ben Glocker, Michael Goetz, Tom Haeck, Hanna-Leena Halme, Mohammad Havaei, Khan M. Iftekharuddin, Pierre‐Marc Jodoin, Konstantinos Kamnitsas, Elias Kellner, Antti Korvenoja, Hugo Larochelle, Christian Ledig, Jia‐Hong Lee, Frederik Maes, Qaiser Mahmood, Klaus Maier‐Hein, Richard McKinley, John Muschelli, Chris Pal, Linmin Pei, Janaki Raman Rangarajan, Syed M. S. Reza, David Robben, Daniel Rueckert, Eero Salli, Paul Suetens, Ching‐Wei Wang, Matthias Wilms, Jan S. Kirschke, Ulrike M. Krämer, Thomas F. Münte, Peter Schramm, Roland Wiest, Heinz Handels, Mauricio Reyes - Medical Image Analysis, Medical Image Anal. 2016 cited by 531

  4. Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice

    Authors: Jeroen Bertels, Tom Eelbode, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops, Matthew B. Blaschko - Lecture notes in computer science, MICCAI (2) 2019 cited by 274

  5. Lipid availability determines fate of skeletal progenitor cells via SOX9

    Authors: Nick van Gastel, Steve Stegen, Guy Eelen, Sandra Schoors, Aurélie Carlier, Veerle W. Daniëls, Ninib Baryawno, Dariusz Przybylski, Maarten Depypere, Pieter‐Jan Stiers, Dennis Lambrechts, Riet Van Looveren, Sophie Torrekens, Azeem Sharda, Patrizia Agostinis, Diether Lambrechts, Frederik Maes, Johan Swinnen, Liesbet Geris, Hans Van Oosterwyck, Bernard Thienpont, Peter Carmeliet, David T. Scadden, Geert Carmeliet - Nature 2020 cited by 236

  6. Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty

    Authors: Adriaan Lambrechts, Roel Wirix-Speetjens, Frederik Maes, Sabine Van Huffel - Frontiers in Robotics and AI, Frontiers Robotics AI 2022 cited by 68

  7. Medical image registration using mutual information

    Authors: Frederik Maes, Dirk Vandermeulen, Paul Suetens - IEEE, Proc. IEEE 2003 cited by 431

  8. Cross-Modal Distillation to Improve MRI-Based Brain Tumor Segmentation With Missing MRI Sequences

    Authors: Masoomeh Rahimpour, Jeroen Bertels, Ahmed Radwan, Henri Vandermeulen, Stefan Sunaert, Dirk Vandermeulen, Frederik Maes, Karolien Goffin, Michel Koole - IEEE Transactions on Biomedical Engineering, IEEE Trans. Biomed. Eng. 2021 cited by 49

  9. Benefits of deep learning for delineation of organs at risk in head and neck cancer

    Authors: Julie van der Veen, Siri Willems, Sarah Deschuymer, David Robben, Wouter Crijns, Frederik Maes, Sandra Nuyts - Radiotherapy and Oncology 2019 cited by 137

  10. Interobserver variability in organ at risk delineation in head and neck cancer

    Authors: Julie van der Veen, Ákos Gulybán, Siri Willems, Frederik Maes, Sandra Nuyts - Radiation Oncology 2021 cited by 89

  11. icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions

    Authors: Mladen Rakić, Sophie Vercruyssen, Simon Van Eyndhoven, Ezequiel de la Rosa, Saurabh Jain, Sabine Van Huffel, Frederik Maes, Dirk Smeets, Diana M. Sima - NeuroImage Clinical 2021 cited by 55

  12. Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model

    Authors: Daan Christiaens, Marco Reisert, Thijs Dhollander, Stefan Sunaert, Paul Suetens, Frederik Maes - NeuroImage 2015 cited by 180

  13. Benefits of automated gross tumor volume segmentation in head and neck cancer using multi-modality information

    Authors: Heleen Bollen, Siri Willems, M. Wegge, Frederik Maes, Sandra Nuyts - Radiotherapy and Oncology 2023 cited by 32

  14. Automated Segmentation of Multiple Sclerosis Lesions by Model Outlier Detection

    Authors: Koen Van Leemput, Frederik Maes, Dirk Vandermeulen, Alan C. F. Colchester, Paul Suetens - IEEE Transactions on Medical Imaging, IEEE Trans. Medical Imaging 2001 cited by 501

  15. Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques

    Authors: Jay B. West, Joan Fitzpatrick, Matthew Y. Wang, Benoît M. Dawant, Calvin R. Maurer, Robert Kessler, Robert J. Maciunas, Christian Barillot, D. Lemoine, André Collignon, Frederik Maes, Paul Suetens, Dirk Vandermeulen, Petra A. van den Elsen, Sandy Napel, Thilaka S. Sumanaweera, Beth A. Harkness, Paul F. Hemler, David Hill, David J. Hawkes, Colin Studholme, J. B. Antoine Maintz, Max A. Viergever, Grégoire Malandain, Xavier Pennec, Marilyn E. Noz, Gerald Q. Maguire, Michael A. Pollack, Charles A. Pelizzari, Richard A. Robb, Dennis P. Hanson, Roger P. Woods - Journal of Computer Assisted Tomography 1997 cited by 888

  16. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images

    Authors: Saurabh Jain, Diana M. Sima, Annemie Ribbens, Melissa Cambron, Anke Maertens, Wim Van Hecke, Johan De Mey, Frederik Barkhof, Martijn D. Steenwijk, Marita Daams, Frederik Maes, Sabine Van Huffel, Hugo Vrenken, Dirk Smeets - NeuroImage Clinical 2015 cited by 224

  17. Pitfalls in training and validation of deep learning systems

    Authors: Tom Eelbode, Pieter Sinonquel, Frederik Maes, Raf Bisschops - Best Practice & Research Clinical Gastroenterology 2020 cited by 37

  18. Deep learning for elective neck delineation: More consistent and time efficient

    Authors: Julie van der Veen, Siri Willems, Heleen Bollen, Frederik Maes, Sandra Nuyts - Radiotherapy and Oncology 2020 cited by 34

  19. Clinical evaluation of a deep learning model for segmentation of target volumes in breast cancer radiotherapy

    Authors: P. Buelens, Siri Willems, Liesbeth Vandewinckele, Wouter Crijns, Frederik Maes, Caroline Weltens - Radiotherapy and Oncology 2022 cited by 24

  20. Bone quality assessment based on cone beam computed tomography imaging

    Authors: Hua Yan, Olivia Nackaerts, Joke Duyck, Frederik Maes, Reinhilde Jacobs - Clinical Oral Implants Research 2009 cited by 174

  21. Optimal 68Ga-PSMA and 18F-PSMA PET window levelling for gross tumour volume delineation in primary prostate cancer

    Authors: Cédric Draulans, Robin De Roover, Uulke A. van der Heide, Linda G.W. Kerkmeijer, Robert Jan Smeenk, Floris J. Pos, Wouter V. Vogel, James Nagarajah, Marcel J. R. Janssen, Sofie Isebaert, Frederik Maes, Cindy Mai, Raymond Oyen, Steven Joniau, Martina Kunze-Busch, Karolien Goffin, Karin Haustermans - European Journal of Nuclear Medicine and Molecular Imaging 2020 cited by 40

  22. Advanced Imaging in Gastrointestinal Endoscopy: A Literature Review of the Current State of the Art

    Authors: Pieter Sinonquel, Séverine Vermeire, Frederik Maes, Raf Bisschops - GE Portuguese Journal of Gastroenterology 2022 cited by 18

  23. Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome

    Authors: Berardino Barile, Pooya Ashtari, Claudio Stamile, Aldo Marzullo, Frederik Maes, Françoise Durand-Dubief, Sabine Van Huffel, Dominique Sappey-Marinier - Frontiers in Robotics and AI, Frontiers Robotics AI 2022 cited by 16

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

    Authors: Koen Van Leemput, Frederik Maes, Dirk Vandermeulen, Paul Suetens - IEEE Transactions on Medical Imaging, IEEE Trans. Medical Imaging 1999 cited by 586