HIST: HyperIntensity Segmentation Tool

  • Jose V. Manjón
  • Pierrick CoupéEmail author
  • Parnesh Raniga
  • Ying Xia
  • Jurgen Fripp
  • Olivier Salvado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9993)


Accurate quantification of white matter hyperintensities (WMH) from MRI is a valuable tool for studies on ageing and neurodegeneration. Reliable automatic extraction of WMH biomarkers is challenging, primarily due to their heterogeneous spatial occurrence, their small size and their diffuse nature. In this paper, we present an automatic and accurate method to segment these lesions that is based on the use of neural networks and an overcomplete strategy. The proposed method was compared to other related methods showing competitive and reliable results in two different neurodegenerative datasets.


White Matter Hyperintensities Flair Image Neural Network Classifier Brain Mask Lesion Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research has been done thanks to the Australian distinguished visiting professor grant and the Spanish “Programa de apoyo a la investigación y desarrollo (PAID-00-15)” of the Universidad Politécnica de Valencia. This study has been carried out with support from the French State, managed by the French National Research Agency in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project “Défi imag’In”.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jose V. Manjón
    • 1
  • Pierrick Coupé
    • 2
    • 3
    Email author
  • Parnesh Raniga
    • 4
  • Ying Xia
    • 4
  • Jurgen Fripp
    • 4
  • Olivier Salvado
    • 4
  1. 1.Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones AvanzadasUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.Univ. Bordeaux, LaBRI, UMR 5800, PICTURATalenceFrance
  3. 3.CNRS, LaBRI, UMR 5800, PICTURATalenceFrance
  4. 4.Australian e-Health Research CentreCSIROBrisbaneAustralia

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