Koen Van Leemput, Ph.D. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital, Harvard Medical School 149 Thirteenth Street, Suite 2301 Charlestown, MA 02129 USA Department of Health Technology Technical University of Denmark Richard Petersens Plads, Building 349 2800 Lyngby, Denmark

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. As opposed to many other groups doing "AI" in this field, 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. Since 2007 I am a faculty member at the Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, and since 2011 also at the Technical University of Denmark. Between 2007 and 2011 I was also a research scientist at the MIT Computer Science and Artificial Intelligence Laboratory. I am the coordinator of the Translational Brain Imaging Training Network (TRABIT), an EU-funded consortium focused on developing new computational methods to analyze the "wild" type of imaging data arising in the standard clinical treatment of brain disorders. 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 (accepted)
	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, 2021 (accepted)
	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