Dirk Vandermeulen
Dirk Vandermeulen is a medical-imaging researcher whose computer-vision work spans neurosurgery and forensic imaging. He began applying computer vision to forensic imaging in 2002 and coauthored two COVID-19 CT studies in 2020, on segmentation and explainable classification. In 2023, he coauthored a genome-wide association study that identified 30 loci linked to cranial-vault shape. In 2024, he was a professor of medical and forensic image analysis at KU Leuven; in 2025, he coauthored an interpretable study of molar staging. As of October 2026, he is a professor at University Hospitals Leuven.
Timeline
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Since 2002
Computer vision in forensics
Initially, his research focuses on image processing for computer-assisted stereotactic neurosurgery. Since 2002, he applies computer vision and medical image analysis to forensic imaging, including craniofacial reconstruction, biometric authentication and facial analysis.
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2020
Deep learning for COVID-19 CT
In 2020, he coauthors a comparison of 12 deep-learning algorithms for segmenting lungs and lesions in COVID-19 chest CT scans. An ensemble produces mean Dice scores of 0.982 for lung segmentation, 0.724 for binary lesion segmentation and 0.469 for multiclass lesion segmentation.
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2020
Explainable CT diagnosis
In 2020, he coauthors an explainable semi-supervised system for classifying COVID-19 from CT images. Variational autoencoders produce feature embeddings, and a convolutional neural network performs classification.
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2023
Genetics of cranial shape
In 2023, he coauthors a multi-ancestry genome-wide association study of 3D cranial-vault shape from MRI scans of 6,772 children. The study identifies 30 genome-wide significant loci.
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2024
Professor at KU Leuven
In 2024, he is a professor of medical and forensic image analysis at KU Leuven's Division for Image and Speech Processing in the Department of Electrical Engineering. He is also an extraordinary professor at the University of Pretoria, South Africa.
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2025
Interpreting dental-stage models
In 2025, he coauthors a forensic dental-age study that combines a convolutional autoencoder and a Vision Transformer to classify second and third molar stages. The framework improves accuracy for both teeth; its latent-space metrics and image reconstructions point to variation within the tooth 38 dataset as a source of the remaining performance gap.
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As of October 2026
Professor at University Hospitals Leuven
As of October 2026, he is a professor at University Hospitals Leuven in Leuven, Belgium.
Career
- NowDepartment of Electrical Engineering, KU LeuvenProfessor of medical and forensic image analysis
- NowUniversity Hospitals LeuvenProfessor
- 2024University of PretoriaExtraordinary Professor
- 2013–2015IMEC
- 1988–2026KU Leuven
Sources and credits
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- Dirk Vandermeulen – IACI 2024
- Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
- Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
- Joint multi-ancestry and admixed GWAS reveals the complex genetics behind human cranial vault shape
- An Autoencoder and Vision Transformer-based Interpretability Analysis of the Differences in Automated Staging of Second and Third Molars
- An autoencoder and vision transformer based interpretability analysis on the performance differences in automated staging of second and third molars
- Loop | Dirk Vandermeulen


