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International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

BrainLes 2016: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries pp 282–290Cite as

Unsupervised 3-D Feature Learning for Mild Traumatic Brain Injury

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10154)

Abstract

We present an unsupervised three-dimensional feature clustering algorithm to gather the mTOP2016 challenge data into 3 groups. We use the brain MR-T1, diffusion tensor fractional anisotropy, and diffusion tensor mean diffusivity images provided by the mTOP2016 competition. A distance-based size constraint method for data clustering is used. The proposed approach achieves 0.267 adjusted rand index and 0.3556 homogeneity score within the 15 labeled subjects, corresponding to 10 correctly classified data items. Based on visual exploration of the data, we believe that a localized analysis of the lesion regions, using the computed tractography data, is a promising direction to pursue.

Keywords

  • Fractional Anisotropy
  • Feature Representation
  • Mild Traumatic Brain Injury
  • Adjusted Rand Index
  • Feature Kernel

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.

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  • DOI: 10.1007/978-3-319-55524-9_26
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Acknowledgments

This research was partially supported by HD059217 from the National Institutes of Health.

Author information

Authors and Affiliations

  1. University of California, Santa Barbara, Santa Barbara, California, 93106, USA

    Po-Yu Kao, Eduardo Rojas, Angela Zhang & B. S. Manjunath

  2. University of California, Irvine, Irvine, California, 92697, USA

    Jefferson W. Chen

Authors
  1. Po-Yu Kao
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  2. Eduardo Rojas
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  3. Jefferson W. Chen
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  4. Angela Zhang
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  5. B. S. Manjunath
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Corresponding author

Correspondence to Po-Yu Kao .

Editor information

Editors and Affiliations

  1. University of Zurich, Istituto Italiano di Tecnologia, Genoa, Italy

    Alessandro Crimi

  2. TU München, Computer Science, Munich, Germany

    Bjoern Menze

  3. Medical Informatics, University of Lübeck, Lübeck, Germany

    Oskar Maier

  4. Surgical Technology and Biomechanics, Universität Bern, Bern, Switzerland

    Mauricio Reyes

  5. Department of Medicine, University of Cambridge, Cambridge, United Kingdom

    Stefan Winzeck

  6. Institute of Medical Informatics, University of Lübeck, Lübeck, Germany

    Heinz Handels

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Kao, PY., Rojas, E., Chen, J.W., Zhang, A., Manjunath, B.S. (2016). Unsupervised 3-D Feature Learning for Mild Traumatic Brain Injury. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_26

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  • DOI: https://doi.org/10.1007/978-3-319-55524-9_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55523-2

  • Online ISBN: 978-3-319-55524-9

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