Diagnosis of Alzheimer's Disease via Multi-Modality 3D Convolutional Neural Network

Topic: Brain imaging, connectivity and assessment

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. In the last decade, studies on AD diagnosis has attached great significance to artificial intelligence-based diagnostic algorithms. Among the diverse modalities of imaging data, T1-weighted MR and FDG-PET are widely used for this task. In this paper, we propose a convolutional neural network (CNN) to integrate all the multi-modality information included in both T1-MR and FDG-PET images of the hippocampal area, for the diagnosis of AD. Different from the traditional machine learning algorithms, this method does not require manually extracted features, instead, it utilizes 3D image-processing CNNs to learn features for the diagnosis or prognosis of AD. To test the performance of the proposed network, we trained the classifier with paired T1-MR and FDG-PET images in the ADNI datasets, including 731 cognitively unimpaired (labeled as CN) subjects, 647 subjects with AD, 441 subjects with stable mild cognitive impairment (sMCI) and 326 subjects with progressive mild cognitive impairment (pMCI). We obtained higher accuracies of 90.10% for CN vs. AD task, 87.46% for CN vs. pMCI task, and 76.90% for sMCI vs. pMCI task. The proposed framework yields a state-of-the-art performance. Finally, the results have demonstrated that (1) segmentation is not a prerequisite when using a CNN for the classification, (2) the combination of two modality imaging data generates better results.

@article{huang2019-31213967,
  author  = {Huang, Y. and Xu, J. and Zhou, Y. and Tong, T. and Zhuang, X.},
  title   = {{Diagnosis of Alzheimer's Disease via Multi-Modality 3D Convolutional Neural Network}},
  journal = {Frontiers in neuroscience},
  year    = {2019},
  doi     = {10.3389/fnins.2019.00509},
  pmid    = {31213967},
  url     = {https://doi.org/10.3389/fnins.2019.00509}
}

Key findings

• Finally, the results have demonstrated that (1) segmentation is not a prerequisite when using a CNN for the classification, (2) the combination of two modality imaging data generates better results.

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Study details

• Participants647
• Effect size90.10%; 87.46%; 76.90%
• Evidence strengthLow

Read the original

• DOI: 10.3389/fnins.2019.00509
• PubMed: 31213967
• Original source →

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