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International MICCAI Brainlesion Workshop

BrainLes 2017: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries pp 489–500Cite as

WMH Segmentation Challenge: A Texture-Based Classification Approach

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10670))

Abstract

This Grand Challenge at MICCAI 2017 aims to directly compare methods for the automatic segmentation of White Matter Hyperintensities (WMH) of presumed vascular origin. Our method automatically segment WMH by using texture-based classification of pixels within the brain white matter. It uses no a priori information about the WMH size, contrast or location. The main goal is to compute the probability of each pixel being normal or WMH tissue, by generating a probability map. Based on this probability map, we can automatically segment the WMHs.

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Notes

  1. 1.

    http://wmh.isi.uu.nl/.

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Acknowledgments

The authors would like to thank Hotchkiss Brain Institute; CAPES process PVE 88881.062158/2014-01; FAPESP processes 2012/21826-1 CEPID2013/07559-3 for providing financial support.

Author information

Authors and Affiliations

  1. Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada

    Mariana Bento, Roberto de Souza & Richard Frayne

  2. Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada

    Mariana Bento & Richard Frayne

  3. School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil

    Mariana Bento, Roberto de Souza, Roberto Lotufo & Letícia Rittner

Authors
  1. Mariana Bento
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  2. Roberto de Souza
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  3. Roberto Lotufo
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  4. Richard Frayne
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  5. Letícia Rittner
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Corresponding author

Correspondence to Mariana Bento .

Editor information

Editors and Affiliations

  1. African Institute of Mathematical Sciences, Cape Town, Ghana

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Spyridon Bakas

  3. University Medical Center Utrecht, Utrecht, The Netherlands

    Hugo Kuijf

  4. Technical University Munich, Munich, Germany

    Bjoern Menze

  5. Universität Bern, Bern, Switzerland

    Mauricio Reyes

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Bento, M., de Souza, R., Lotufo, R., Frayne, R., Rittner, L. (2018). WMH Segmentation Challenge: A Texture-Based Classification Approach. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_41

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  • DOI: https://doi.org/10.1007/978-3-319-75238-9_41

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