期刊
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
卷 47, 期 9, 页码 2197-2206出版社
SPRINGER
DOI: 10.1007/s00259-019-04676-y
关键词
Alzheimer's disease; [F-18] FDG PET; CT; Generative Adversarial Network; External validation; Feasibility study
资金
- Basic Science Research Program through the National Research Foundation of Korea - Ministry of Science and ICT [NRF-2018M3C7A1056898]
- Bio & Medical Technology Development Program of theNational Research Foundation (NRF) - Ministry of Science and ICT [NRF-2018M3A9H6081483]
- National Research Foundation of Korea [2018M3C7A1056898] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Purpose The aim of this feasibility study was to use slice selective learning using a Generative Adversarial Network for external validation. We aimed to build a model less sensitive to PET imaging acquisition environment, since differences in environments negatively influence network performance. To investigate the slice performance, each slice evaluation was performed. Methods We trained our model using a 18F-fluorodeoxyglucose ([F-18]FDG) PET/CT dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and tested the model with a Severance Hospital dataset. We applied slice selective learning to reduce computational cost and to extract unbiased features. We extracted features of Alzheimer's disease (AD) and normal cognitive (NC) condition using a Boundary Equilibrium Generative Adversarial Network (BEGAN) for stable convergence. Then, we utilized these features to train a support vector machine (SVM) classifier to distinguish AD from NC. Results The slice range that covered the posterior cingulate cortex (PCC) using double slices showed the best performance. The accuracy, sensitivity, and specificity of our proposed network was 94.33%, 91.78%, and 97.06% using the Severance dataset and 94.82%, 92.11%, and 97.45% using the ADNI dataset. The performance on the two independent datasets showed no statistical difference (p > 0.05). Moreover, there was a statistical difference in the performance between using two slices and one slice as input (p < 0.05). Conclusions Our model learned the generalized features of AD and NC for external validation when appropriate slices were selected. This study showed the feasibility of this model with consistent performance when tested using datasets acquired from a variety of image-acquisition environments.
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