Koen's picture

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


Software

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

Teaching


Selected publications   [More...]

New: 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

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