Performance of five automated white matter hyperintensity segmentation methods in a multicenter dataset

  • Rutger Heinen*
  • , Martijn D. Steenwijk
  • , TRACE-VCI Study Grp
  • , Frederik Barkhof
  • , J. Matthijs Biesbroek
  • , Wiesje M. van der Flier
  • , H. J. Kuijf
  • , N. D. Prins
  • , Hugo Vrenken
  • , Geert Jan Biessels
  • , Jeroen de Bresser
  • , E. van den Berg
  • , J. M. F. Boomsma
  • , L. G. Exalto
  • , D. A. Ferro
  • , C. J. M. Frijns
  • , O. N. Groeneveld
  • , N. M. van Kalsbeek
  • , J. H. Verwer
  • , J. de Bresser
    • H. J. Kuijf, M. E. Emmelot-Vonk, H. L. Koek, M. R. Benedictus, J. Bremer, A. E. Leeuwis, J. Leijenaar, N. D. Prins, P. Scheltens, B. M. Tijms, M. P. Wattjes, C. E. Teunissen, T. Koene, J. M. F. Boomsma, H. C. Weinstein, M. Hamaker, R. Faaij, M. Pleizier, M. Prins, E. Vriens
*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

47 Citations (Scopus)
45 Downloads (Pure)

Fingerprint

Dive into the research topics of 'Performance of five automated white matter hyperintensity segmentation methods in a multicenter dataset'. Together they form a unique fingerprint.
Sort by

Medicine and Dentistry

Neuroscience

Immunology and Microbiology