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Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble

  • Yani Chen
  • Bibo Shi
  • Zhewei Wang
  • Tao Sun
  • Charles D. Smith
  • Jundong LiuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10541)

Abstract

In this work, a novel deep neural network is developed to automatically segment human hippocampi from MR images. To take advantage of the efficiency of 2D convolutional operations, as well the inter-slice dependence within 3D volumes, our model stacks fully convolutional neural networks (CNN) through convolutional long short-term memory (CLSTM) to extract voxel labels. Enhanced slice-wise label consistency is ensured, leading to improved segmentation stability and accuracy. We apply our model on ADNI dataset, and demonstrate that our proposed model outperforms the state-of-the-art solutions.

Keywords

Hippocampus segmentation Brain MRI CNN LSTM 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yani Chen
    • 1
  • Bibo Shi
    • 2
  • Zhewei Wang
    • 1
  • Tao Sun
    • 1
  • Charles D. Smith
    • 3
  • Jundong Liu
    • 1
    Email author
  1. 1.School of Electrical Engineering and Computer ScienceOhio UniversityAthensUSA
  2. 2.Department of RadiologyDuke UniversityDurhamUSA
  3. 3.Department of NeurologyUniversity of KentuckyLexingtonUSA

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