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Professor of AI-Health Department of Neuroscience and Biomedical Engineering Department of Computer Science Aalto University Rakentajanaukio 2C (F309) 02150 Espoo, Finland koen.vanleemput (at) aalto.fi |
Koen Van Leemput is a Full Professor with Aalto University's School of Science and leads the Medical Image Computing group in Espoo, Finland. His primary research focus is on developing computational methods and tools to extract relevant information from medical images. He is internationally recognized for his research contributions to generative probabilistic models for medical image analysis, in particular for measuring and interpreting images of the human brain. Many of the methods he has developed are included in leading open-source software packages such as FreeSurfer and SimNIBS.
Dr. Van Leemput received the MSc and PhD degrees in Electrical Engineering in 1997 and 2001, respectively, from the KU Leuven in Belgium. Prior to joining Aalto University in 2023, he was at the Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School (since 2007), and at Technical University of Denmark (since 2011). He has also worked as a research scientist at the Massachusetts Institute of Technology (2007-2010) and the Helsinki University Central Hospital (2002-2007).
Koen Van Leemput has served on the editorial board and program committee of the most important journals and conferences in his field, including the IEEE Transactions on Medical Imaging, the Medical Image Analysis journal, and the Medical Image Computing and Computer-Assisted Inventions (MICCAI) conference. He has been the principal investigator of several large research projects both in the US and in Europe, most recently of an international training network of 15 PhD students funded by the EU.
Our contrast-adaptive whole-brain segmentation method Samseg, including extensions for segmenting multiple sclerosis (MS) lesions and longitudinal scans, is available from FreeSurfer. The same underlying software is also used in FreeSurfer for segmenting hippocampal subfields and nuclei of the amygdala, as well as brainstem substructures and thalamic nuclei.
A special version of Samseg to segment head structures relevant for transcranial brain stimulation is included in SimNIBS.
I teach the yearly Medical Image Analysis course at Aalto (NBE-E4010). The teaching material used in the course consists of a tailor-made book and lecture slides, which are freely available under a CC BY 4.0 license.
The research in my group focuses on developing computational methods for analyzing organ-level medical images. Our aim is to significantly augment the capabilities of human experts, such as neuroscientists and clinicians, by analyzing images much faster, much more consistently, and in much more detail than they would otherwise be able to. This should ultimately enable new scientific insights into disease mechanisms, more efficient clinical trials, and improved treatment outcomes in individual patients.
The typical applications we address are automatic segmentation (delineating and measuring anatomical structures), registration (locally deforming images to match their content), subject-level prediction (automatic diagnosis or prognosis), and image reconstruction (turning physical measurements into images). Across all these applications, the focus of the group is on generative probabilistic models, as these excel at generalizing across scanners/protocols/diseases, at providing interpretable results that align intuitively with human reasoning, and at quantifying the uncertainty associated with the obtained results.
For my full list publication, see my Google scholar profile. Below I hightlight a few key publications addressing common themes in medical image computing.
Seldom addressed in academic benchmarks, but immediately surfacing in real-world deployments: the imaging zoo resulting from using different scanners, contrast mechanisms, contrast combinations, image resolutions, imaging modalities, patient populations, timing and number of follow-up scans in longitudinal imaging, ... The compositionality of generative models can deal with this without a combinatorial explosion of training data, often with no or only minimal changes to the actual software:
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An open-source tool for longitudinal whole-brain and white matter lesion segmentation.
S. Cerri, D.N. Greve, A. Hoopes, H. Lundell, H.R. Siebner, M. Muehlau, K. Van Leemput. NeuroImage: Clinical, vol. 38, 103354, 2023 |
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A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis.
S. Cerri, O. Puonti, D.S. Meier, J. Wuerfel, M. Muehlau, H.R. Siebner, K. Van Leemput. NeuroImage, vol. 225, 117471, 2021 |
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A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning.
M. Agn, P.M. af Rosenschold, O. Puonti, M.J. Lundemann, L. Mancini, A. Papadaki, S. Thust, J. Ashburner, I. Law, K. Van Leemput. Medical Image Analysis, vol. 54, pp. 220-237, 2019 |
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Fast and Sequence-Adaptive Whole-Brain Segmentation Using Parametric Bayesian Modeling. O. Puonti, J. E. Iglesias, K. Van Leemput. NeuroImage, vol. 143, pp. 235-249, 2016 |
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A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI.
J.E. Iglesias, J.C. Augustinack, K. Nguyen, C.M. Player, A. Player, M. Wright, N. Roy, M.P. Frosch, A.C. McKee, L.L. Wald, B. Fischl, and K. Van Leemput. NeuroImage, vol. 115, pp. 117-137, 2015 |
Before automatic diagnostic systems can safely be adopted for widespread clinical use, they should not only predict well, but also explain their predictions in terms that are biologically meaningful. Generative models excel at this task, by mimicking the way humans look for relevant cause-effect relationships while ignoring irrelevant variations in the data:
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A lightweight generative model for interpretable subject-level prediction.
C. Mauri, S. Cerri, O. Puonti, M. Muehlau, K. Van Leemput. Medical Image Analysis, vol. 101, 103436, 2025 |
The ability to provide end-users with reliable uncertainty estimates is a prerequisite in many clinical applications (e.g., when making life-changing treatment decisions). Generative models provide a principled way to compute accurate "error bars" using Markov chain Monte Carlo (MCMC) sampling:
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Fast Nonparametric Mutual-Information-based Registration and Uncertainty Estimation.
M. Agn and K. Van Leemput. MICCAI2019 UNSURE workshop, Lecture Notes in Computer Science, vol. 11840, pp. 42-51, 2019 |
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Improved Inference in Bayesian Segmentation Using Monte Carlo Sampling: Application to Hippocampal Subfield Volumetry.
J. E. Iglesias, M. R. Sabuncu, K. Van Leemput. Medical Image Analysis, vol. 17, no. 8, pp. 1181-1191, 2013 |
We have analyzed some well-known algorithms over the years to show why they work (or sometimes don't!):
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Analysis of the Haufe transformation. (Don't use it!)
Appendix D of: C. Mauri, S. Cerri, O. Puonti, M. Muehlau, K. Van Leemput. Medical Image Analysis, vol. 101, 103436, 2025 |
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A Cautionary Analysis of STAPLE Using Direct Inference of Segmentation Truth.
K. Van Leemput and M.R. Sabuncu. MICCAI2014, Lecture Notes in Computer Science, vol. 8673, pp. 398-406, 2014 |
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N3 Bias Field Correction Explained as a Bayesian Modeling Method.
C.T. Larsen, J.E. Iglesias, and K. Van Leemput. MICCAI2014 BAMBI Workshop, Lecture Notes in Computer Science, vol. 8677, pp. 1-12, 2014 |
I am one of the instigators of the well-known Multimodal Brain Tumor Segmentation Challenge (BRATS) series organized yearly in conjunction with MICCAI:
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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).
B. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, [...], M. Prastawa, M. Reyes, K. Van Leemput. IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp. 1993-2024, 2015 |