Abstract
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.
Highlights
Aging of the global population results in an increasing number of people with dementia
The shrinkage of the hippocampi is the best-established magnetic resonance imaging (MRI) biomarker to stage the progression of Alzheimer’s disease (AD) (Jack et al, 2011a), and the only MRI biomarker qualified for the enrichment of clinical trials (Hill et al, 2014)
Models obtained from the Small Reception Field (SmallRF) and Big Reception Field (BigRF) groups had similar performances, the regions of interest (ROIs) of positron emission tomography (PET) was chosen to be the same as the MRI’s, because the ROI of SmallRF was voxelwisely aligned with the ROI of the paired MRI
Summary
Aging of the global population results in an increasing number of people with dementia. Recent studies indicate that 50 million people are living with dementia (Patterson, 2018), of whom 60– 70% have Alzheimer’s Disease (AD) (World Health Organization, 2012). Known as one of the most common neurodegenerative diseases, AD can result in severe cognitive impairment and behavioral issues. Mild cognitive impairment (MCI) is a neurological disorder, which may occur as a transitional stage between normal aging and the preclinical phase of dementia. MCI is a heterogeneous group and can be classified according to its various clinical outcomes (Huang et al, 2003). We partitioned MCI into progressive MCI (pMCI) and stable MCI (sMCI), which are retrospective diagnostic terms based on the clinical follow-up according to the DSM-5 criteria (American Psychiatric Association, 2013).
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