Multi-View Separable Pyramid Network for AD Prediction at MCI Stage by 18F-FDG Brain PET Imaging

Alzheimer's Disease (AD), one of the main causes of death in elderly people, is characterized by Mild Cognitive Impairment (MCI) at prodromal stage. Nevertheless, only part of MCI subjects could progress to AD. The main objective of this paper is thus to identify those who will develop a dement...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2021-01, Vol.40 (1), p.81-92
Main Authors: Pan, Xiaoxi, Phan, Trong-Le, Adel, Mouloud, Fossati, Caroline, Gaidon, Thierry, Wojak, Julien, Guedj, Eric
Format: Article
Language:English
Subjects:
Algorithms
Alzheimer's disease
Cognitive ability
Convolution
Decision making
Deep learning
Dementia
Dementia disorders
F-FDG PET
Feature extraction
Fluorine isotopes
Impairment
Machine learning
Magnetic resonance imaging
Medical imaging
mild cognitive impairment
Neurodegenerative diseases
Neuroimaging
Older people
Positron emission
Positron emission tomography
Separable convolution
slice-wise CNN
Spatial data
spatial-wise CNN
Three-dimensional displays
Tomography
Two dimensional displays
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Summary:Alzheimer's Disease (AD), one of the main causes of death in elderly people, is characterized by Mild Cognitive Impairment (MCI) at prodromal stage. Nevertheless, only part of MCI subjects could progress to AD. The main objective of this paper is thus to identify those who will develop a dementia of AD type among MCI patients. 18 F-FluoroDeoxyGlucose Positron Emission Tomography ( 18 F-FDG PET) serves as a neuroimaging modality for early diagnosis as it can reflect neural activity via measuring glucose uptake at resting-state. In this paper, we design a deep network on 18 F-FDG PET modality to address the problem of AD identification at early MCI stage. To this end, a Multi-view Separable Pyramid Network (MiSePyNet) is proposed, in which representations are learned from axial, coronal and sagittal views of PET scans so as to offer complementary information and then combined to make a decision jointly. Different from the widely and naturally used 3D convolution operations for 3D images, the proposed architecture is deployed with separable convolution from slice-wise to spatial-wise successively, which can retain the spatial information and reduce training parameters compared to 2D and 3D networks, respectively. Experiments on ADNI dataset show that the proposed method can yield better performance than both traditional and deep learning-based algorithms for predicting the progression of Mild Cognitive Impairment, with a classification accuracy of 83.05%.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2020.3022591