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Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis

  • Rongjian Li
  • Wenlu Zhang
  • Heung-Il Suk
  • Li Wang
  • Jiang Li
  • Dinggang Shen
  • Shuiwang Ji
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

Combining multi-modality brain data for disease diagnosis commonly leads to improved performance. A challenge in using multi-modality data is that the data are commonly incomplete; namely, some modality might be missing for some subjects. In this work, we proposed a deep learning based framework for estimating multi-modality imaging data. Our method takes the form of convolutional neural networks, where the input and output are two volumetric modalities. The network contains a large number of trainable parameters that capture the relationship between input and output modalities. When trained on subjects with all modalities, the network can estimate the output modality given the input modality. We evaluated our method on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, where the input and output modalities are MRI and PET images, respectively. Results showed that our method significantly outperformed prior methods.

Keywords

Positron Emission Tomography Mild Cognitive Impairment Positron Emission Tomography Image Magnetic Resonance Imaging Data Convolutional Neural Network 
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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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rongjian Li
    • 1
  • Wenlu Zhang
    • 1
  • Heung-Il Suk
    • 2
  • Li Wang
    • 2
  • Jiang Li
    • 3
  • Dinggang Shen
    • 2
  • Shuiwang Ji
    • 1
  1. 1.Department of Computer ScienceOld Dominion UniversityNorfolkUSA
  2. 2.Department of RadiologyUniversity of North Carolina at Chapel HillUSA
  3. 3.Department of ECEOld Dominion UniversityNorfolkUSA

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