Koen Van Leemput

Summary

Affiliation: Harvard University
Country: USA

Publications

  1. ncbi Encoding probabilistic brain atlases using Bayesian inference
    Koen Van Leemput
    Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
    IEEE Trans Med Imaging 28:822-37. 2009
  2. ncbi Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI
    Koen Van Leemput
    Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
    Hippocampus 19:549-57. 2009
  3. ncbi Model-based segmentation of hippocampal subfields in ultra-high resolution in vivo MRI
    Koen Van Leemput
    Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, MGH, Harvard Medical School, USA
    Med Image Comput Comput Assist Interv 11:235-43. 2008
  4. ncbi Asymmetric image-template registration
    Mert R Sabuncu
    Computer Science and Artificial Intelligence Lab, MIT, Harvard Medical School, USA
    Med Image Comput Comput Assist Interv 12:565-73. 2009
  5. ncbi The relevance voxel machine (RVoxM): a self-tuning Bayesian model for informative image-based prediction
    Mert R Sabuncu
    Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
    IEEE Trans Med Imaging 31:2290-306. 2012
  6. ncbi Incorporating parameter uncertainty in Bayesian segmentation models: application to hippocampal subfield volumetry
    Juan Eugenio Iglesias
    Martinos Center for Biomedical Imaging, MGH, Harvard Medical School, USA
    Med Image Comput Comput Assist Interv 15:50-7. 2012
  7. ncbi The Relevance Voxel Machine (RVoxM): a Bayesian method for image-based prediction
    Mert R Sabuncu
    Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, MGH, Harvard Medical School, USA
    Med Image Comput Comput Assist Interv 14:99-106. 2011
  8. ncbi Predicting the location of entorhinal cortex from MRI
    Bruce Fischl
    Athinoula A Martinos Center, Department of Radiology, MGH, Harvard Medical School, Charlestown, MA 02129, USA
    Neuroimage 47:8-17. 2009

Detail Information

Publications8

  1. ncbi Encoding probabilistic brain atlases using Bayesian inference
    Koen Van Leemput
    Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
    IEEE Trans Med Imaging 28:822-37. 2009
    ..We also present experiments of the proposed atlas construction technique in 3-D, and show the resulting atlases' potential in fully-automated, pulse sequence-adaptive segmentation of 36 neuroanatomical structures in brain MRI scans...
  2. ncbi Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI
    Koen Van Leemput
    Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
    Hippocampus 19:549-57. 2009
    ..Unlike manual segmentations, our automated technique is fully reproducible, and fast enough to enable routine analysis of the hippocampal subfields in large imaging studies...
  3. ncbi Model-based segmentation of hippocampal subfields in ultra-high resolution in vivo MRI
    Koen Van Leemput
    Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, MGH, Harvard Medical School, USA
    Med Image Comput Comput Assist Interv 11:235-43. 2008
    ..We validate the proposed technique by comparing our segmentation results with corresponding manual delineations in ultra-high resolution MRI scans of five individuals...
  4. ncbi Asymmetric image-template registration
    Mert R Sabuncu
    Computer Science and Artificial Intelligence Lab, MIT, Harvard Medical School, USA
    Med Image Comput Comput Assist Interv 12:565-73. 2009
    ..We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates...
  5. ncbi The relevance voxel machine (RVoxM): a self-tuning Bayesian model for informative image-based prediction
    Mert R Sabuncu
    Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
    IEEE Trans Med Imaging 31:2290-306. 2012
    ..Our results indicate that RVoxM yields biologically meaningful models, while providing state-of-the-art predictive accuracy...
  6. ncbi Incorporating parameter uncertainty in Bayesian segmentation models: application to hippocampal subfield volumetry
    Juan Eugenio Iglesias
    Martinos Center for Biomedical Imaging, MGH, Harvard Medical School, USA
    Med Image Comput Comput Assist Interv 15:50-7. 2012
    ..As an additional benefit, the method also yields informative "error bars" on the segmentation results for each of the individual sub-structures...
  7. ncbi The Relevance Voxel Machine (RVoxM): a Bayesian method for image-based prediction
    Mert R Sabuncu
    Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, MGH, Harvard Medical School, USA
    Med Image Comput Comput Assist Interv 14:99-106. 2011
    ..Experiments on age prediction from structural brain MRI indicate that RVoxM yields biologically meaningful models that provide excellent predictive accuracy...
  8. ncbi Predicting the location of entorhinal cortex from MRI
    Bruce Fischl
    Athinoula A Martinos Center, Department of Radiology, MGH, Harvard Medical School, Charlestown, MA 02129, USA
    Neuroimage 47:8-17. 2009
    ....