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Unsupervised 3-D Feature Learning for Mild Traumatic Brain Injury

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


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.


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

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

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Correspondence to Po-Yu Kao .

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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.

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