Combining Deep Learning Networks with Permutation Tests to Predict Traumatic Brain Injury Outcome

  • Y. CaiEmail author
  • S. Ji
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)


Reliable prediction of traumatic brain injury (TBI) outcome using neuroimaging is clinically important, yet, computationally challenging. To tackle this problem, we developed an injury prediction or classification pipeline based on diffusion tensor imaging (DTI) by combining a novel deep learning approach with statistical permutation tests. We first applied a multi-modal deep learning network to individually train a classification model for each DTI measure. Individual results were then combined to allow iterative refinement of the classification via Tract-Based Spatial Statistics (TBSS) permutation tests, where voxel sum of skeletonized significance values served as a classification performance feedback. Our technique combined a high-performance machine learning algorithm with a conventional statistical tool, which provided a flexible and intuitive approach to predict TBI outcome.


Brain image analysis Brain injury Deep learning Permutation test 



Funding is provided by the NIH grants R01 NS092853 and R21 NS088781.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringWorcester Polytechnic InstituteWorcesterUSA
  2. 2.Thayer School of EngineeringDartmouth CollegeHanoverUSA

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