World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
20335
World Ranking
9019
National Ranking
3830

Overview

Koen Van Leemput is affiliated with Harvard University in the United States. Their research spans multiple disciplines, primarily focusing on Medicine and Computer Science. Within these fields, their work addresses several subfields including Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, and Neurology.

The scholar's research covers key topics such as Radiomics and Machine Learning in Medical Imaging, Medical Image Segmentation Techniques, Glioma Diagnosis and Treatment, Medical Imaging and Analysis, Advanced Neural Network Applications, Advanced Neuroimaging Techniques and Applications, and Functional Brain Connectivity Studies.

Their recent publications illustrate this focus with papers appearing in prominent venues. Notable works include:

  • SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining (2023, Medical Image Analysis)
  • Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling (2020, NeuroImage)
  • A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis (2020, NeuroImage)
  • A Learning Strategy for Contrast-agnostic MRI Segmentation (2020, arXiv (Cornell University))
  • The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) (2023, arXiv (Cornell University))

Koen Van Leemput has collaborated frequently with several coauthors, with the highest number of joint publications occurring with:

  • Juan Eugenio Iglesias (30 publications)
  • Adrian V. Dalca (14 publications)
  • Jake Albrecht (14 publications)
  • Verena Chung (14 publications)
  • Zhifan Jiang (13 publications)

Their work has been published extensively in venues recognized for research in biomedical imaging and related fields including:

  • arXiv (Cornell University) with 19 publications
  • bioRxiv (Cold Spring Harbor Laboratory) with 5 publications
  • NeuroImage with 4 publications
  • The Journal of Machine Learning for Biomedical Imaging with 3 publications
  • Medical Image Analysis with 2 publications

In addition to journal articles, they have contributed to several book publications through Springer Science+Business Media. Titles include:

  • Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis (2020)
  • Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis (2021)
  • Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (2022)

Best Publications

  • The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    Bjoern H. Menze;Andras Jakab;Stefan Bauer;Jayashree Kalpathy-Cramer

  • The Multimodal Brain TumorImage Segmentation Benchmark (BRATS)

    Bjoern Menze;Mauricio Reyes;Koen Van Leemput;Nicole Porz

  • Automated model-based tissue classification of MR images of the brain

    K. Van Leemput;F. Maes;D. Vandermeulen;P. Suetens

  • A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI

    Juan Eugenio Iglesias;Jean C. Augustinack;Khoa Nguyen;Christopher M. Player

  • Automated model-based bias field correction of MR images of the brain

    K. Van Leemput;F. Maes;D. Vandermeulen;P. Suetens

  • Automated segmentation of multiple sclerosis lesions by model outlier detection

    K. Van Leemput;F. Maes;D. Vandermeulen;A. Colchester

  • A Generative Model for Image Segmentation Based on Label Fusion

    Mert R Sabuncu;B T Thomas Yeo;K Van Leemput;Bruce Fischl

  • Automated segmentation of hippocampal subfields from ultra‐high resolution in vivo MRI

    Koen Van Leemput;Koen Van Leemput;Akram Bakkour;Thomas Benner;Graham Wiggins

  • A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology.

    Juan Eugenio Iglesias;Ricardo Insausti;Garikoitz Lerma-Usabiaga;Martina Bocchetta

  • A unifying framework for partial volume segmentation of brain MR images

    K. Van Leemput;F. Maes;D. Vandermeulen;P. Suetens

  • Quantitative Comparison of 21 Protocols for Labeling Hippocampal Subfields and Parahippocampal Subregions in In Vivo MRI: Towards a Harmonized Segmentation Protocol

    Paul A. Yushkevich;Robert S C Amaral;Jean C. Augustinack;Andrew R. Bender

  • Automatic brain tumor segmentation by subject specific modification of atlas priors.

    Marcel Prastawa;Elizabeth Bullitt;Nathan Moon;Koen Van Leemput

  • Bayesian segmentation of brainstem structures in MRI.

    Juan Eugenio Iglesias;Koen Van Leemput;Priyanka Bhatt;Christen Casillas

  • A generative model for brain tumor segmentation in multi- modal images

    Bjoern H. Menze;Koen Van Leemput;Danial Lashkari;Marc-André Weber

  • Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling.

    Oula Puonti;Juan Eugenio Iglesias;Koen Van Leemput;Koen Van Leemput

  • Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling.

    Oula Puonti;Oula Puonti;Koen Van Leemput;Koen Van Leemput;Guilherme B Saturnino;Guilherme B Saturnino;Hartwig R Siebner;Hartwig R Siebner

  • Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases.

    Juan Eugenio Iglesias;Koen Van Leemput;Jean Augustinack;Ricardo Insausti

  • Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference

    Jana Lipkova;Panagiotis Angelikopoulos;Stephen Wu;Esther Alberts

  • Is Synthesizing MRI Contrast Useful for Inter-modality Analysis?

    Juan Eugenio Iglesias;Ender Konukoglu;Darko Zikic;Ben Glocker

  • Automatic brain and tumor segmentation

    Nathan Moon;Elizabeth Bullitt;Koen Van Leemput;Guido Gerig

  • Patch-based generation of a pseudo CT from conventional MRI sequences for MRI-only radiotherapy of the brain.

    Daniel Andreasen;Koen Van Leemput;Rasmus H. Hansen;Jon A. L. Andersen

  • Model-based brain and tumor segmentation

    N. Moon;E. Bullitt;K. van Leemput;G. Gerig

Frequent Co-Authors

Bruce Fischl
Bruce Fischl Harvard University
Frederik Maes
Frederik Maes KU Leuven
Bjoern H. Menze
Bjoern H. Menze University of Zurich
Paul Suetens
Paul Suetens KU Leuven
Lawrence L. Wald
Lawrence L. Wald Harvard University
Jean C. Augustinack
Jean C. Augustinack Harvard University
Nicholas Ayache
Nicholas Ayache French Institute for Research in Computer Science and Automation - INRIA
Douglas N. Greve
Douglas N. Greve Harvard University

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