PeopleofSuperintelligence

Researcher

Frederik Maes

Frederik Maes is a medical-image computing researcher whose work spans brain MRI, medical image analysis and clinical AI. He coauthored methods for tracking brain MRI lesions, increasing MR spectroscopy resolution, and comparing deep-learning algorithms for COVID-19 CT images. Later work applied AI to knee-replacement planning and developed confidence-aware estimates for ratio-based biomarkers; by 2025, his publication record included at least 140 papers. As of October 2026, he researches medical imaging with KU Leuven’s ESAT/PSI and UZ Leuven’s Medical Imaging Research Center, including a deep-learning pipeline for micro-CT airway analysis.

Now at
UZ Leuven
Works
718
Citations
22,391
h-index
61
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 2016

    MSmetrix-long lesion segmentation

    In 2016, he coauthors MSmetrix-long, a joint expectation-maximization framework for segmenting white-matter lesions across two time points in brain MRI.

  2. 2017

    Super-resolution for MRSI

    In 2017, he coauthors a patch-based method that increases the spatial resolution of metabolite maps from magnetic resonance spectroscopic imaging.

  3. 2020

    Deep learning for COVID-19 CT

    In 2020, he coauthors a study comparing twelve deep-learning algorithms for automatic segmentation of lungs, lesions and lesion types in CT scans from COVID-19 patients.

  4. 2022

    AI knee replacement planning

    In 2022, he coauthors an AI-driven planning study for total knee arthroplasty that evaluates surgeon- and patient-specific preoperative plans requiring fewer changes by the surgeon.

  5. 2025

    Confidence-aware biomarkers

    In 2025, he coauthors a unified confidence-aware framework for estimating ratio-based biomarkers.

  6. By 2025

    At least 140 papers

    By 2025, his publication record spans at least 140 papers, with work dating back to 1995.

  7. As of October 2026

    Medical imaging at KU Leuven

    He is affiliated with KU Leuven’s ESAT/PSI and UZ Leuven’s Medical Imaging Research Center. In August 2026, he coauthors an automated deep-learning pipeline for 3D characterization of the murine upper airway from micro-CT.

Career

  1. Since 1995KU LeuvenResearcher
  2. NowUZ LeuvenResearcher
  3. 2013–2016IMEC
  4. 2009–2012HOGENT University of Applied Sciences and Arts
  5. 2009–2012Ghent University

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes KU Leuven. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  2. Universities keep AI researchers for a median of 6 years, compared with 2 years at frontier labsIncludes KU Leuven. Across 15,811 dated work stays (6,529 people), median role tenure runs from 6 years at universities to 2 years at frontier labs. We compare all five organization types, starting decades from the 1970s to the 2020s, and the organizations that keep people longest.
  3. The United States employs an estimated 48% of people shaping AIIncludes KU Leuven. We estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes KU Leuven. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework frontiersin.org. Checked 21 October 2026.
  2. Patch-Based Super-Resolution of MR Spectroscopic Images frontiersin.org. Checked 21 October 2026.
  3. Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients arxiv.org. Checked 21 October 2026.
  4. Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty frontiersin.org. Checked 21 October 2026.
  5. Beyond Segmentation: Confidence-Aware and Debiased Estimation of Ratio-based Biomarkers arxiv.org. Checked 21 October 2026.
  6. Frederik Maes csauthors.net. Checked 21 October 2026.
  7. An automated deep learning-based pipeline for 3D characterisation of the murine upper airway from micro-CT frontiersin.org. Checked 21 October 2026.