Koen Van Leemput's Homepage

Koen Van Leemput, Ph.D.

Department of Neuroscience and Biomedical Engineering
Aalto University
Rakentajanaukio 2C
02150 Espoo, Finland

Athinoula A. Martinos Center for Biomedical Imaging
Massachusetts General Hospital, Harvard Medical School
149 Thirteenth Street, Suite 2301
Charlestown, MA 02129 USA

Open positions: We currently have several openings (both PhD students and postdocs) in Helsinki, Finland!

Short bio

I am an academic with a focus on medical image computing in general, and neuroimage analysis in particular. Together with my students I develop computational models and methods to extract relevant information from medical images. We are particularly interested in methods that extrapolate to clinical settings, i.e., that can handle pathologies and that work out-of-the-box on data acquired with different scanning hardware, software and protocols. Many of the methods we develop are included in the open-source neuroimage analysis software suite FreeSurfer.

I obtained my PhD degree from the KU Leuven, Belgium, in 2001. In 2007 I became a faculty member at the Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, and a research scientist at the MIT Computer Science and Artificial Intelligence Laboratory. Between 2010 and 2023 I was a professor at the Technical University of Denmark, and currently I am at Aalto University in the capital area of Finland.

I serve as an associate editor of the IEEE Transactions on Medical Imaging, as a member of the editorial board of the Medical Image Analysis Journal, and as a reviewer for several international medical imaging journals and conferences.

Recent talks

2022 symposium of the Danish Society of Medical Physics: Four Decades of Automated Image Registration and Segmentation - slides (PowerPoint, 24M) - slides without movies (PDF, 6M)
Intro to "deep" vs. "classical" computational neuroimaging at the 6th Congress of the European Academy of Neurology (EAN2020): Automated Image Analysis in Neurology: Principles and Practice - slides (PDF, 2M)
Keynote at the MICCAI 2019 Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: Input and Output Uncertainty in Medical Image Analysis - slides (PowerPoint, 79M) - slides without movies (PDF, 7M) - video recording

Software

Many of the methods we develop are distributed through the open-source software suite FreeSurfer, in particular:

The contrast-adaptive whole-brain segmentation method SAMSEG, including extensions for segmenting multiple sclerosis (MS) lesions and longitudinal scans. A special version of SAMSEG to segment head structures relevant for transcranial brain stimulation is included in SimNIBS.
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.

Teaching

DTU Course 22525: Medical Image Analysis. Lecture slides + book are available: http://nmr.mgh.harvard.edu/~koen/mia/.

Selected publications [More...]

	New: Accurate and Explainable Image-based Prediction Using a Lightweight Generative Model C. Mauri, S. Cerri, O. Puonti, M. Muehlau, and K. Van Leemput MICCAI 2022 (oral)
	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
	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
	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
	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
	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
	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
	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
	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
	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
	The Relevance Voxel Machine (RVoxM): A Self-tuning Bayesian Model for Informative Image-based Prediction M.R. Sabuncu and K. Van Leemput IEEE Transactions on Medical Imaging, vol. 31, no. 12, pp. 2290-2306, December 2012
	Encoding Probabilistic Brain Atlases Using Bayesian Inference K. Van Leemput IEEE Transactions on Medical Imaging, vol. 28, no. 6, pp. 822-837, June 2009
	A Unifying Framework for Partial Volume Segmentation of Brain MR Images K. Van Leemput, F. Maes, D. Vandermeulen, P. Suetens IEEE Transactions on Medical Imaging, vol. 22, no. 1, pp. 105-119, January 2003 java applet (with source code)
	Automated Segmentation of Multiple Sclerosis Lesions by Model Outlier Detection K. Van Leemput, F. Maes, D. Vandermeulen, A. Colchester, P. Suetens IEEE Transactions on Medical Imaging, vol. 20, no. 8, pp. 677-688, August 2001
	Automated Model-Based Tissue Classification of MR Images of the Brain K. Van Leemput, F. Maes, D. Vandermeulen, P. Suetens IEEE Transactions on Medical Imaging, vol. 18, no. 10, pp. 897-908, October 1999
	Automated Model-Based Bias Field Correction of MR Images of the Brain K. Van Leemput, F. Maes, D. Vandermeulen, P. Suetens IEEE Transactions on Medical Imaging, vol. 18, no. 10, pp. 885-896, October 1999