Abstract
Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.
Highlights
The National Institute on Aging and Alzheimer’s Association (NIA-AA) defines three stages of Alzheimer disease (AD) on the basis of pathobiology and clinical symptoms [1]
We have evaluated 36 different models trained for six different region of interest (ROI) and six classification tasks
In addition to the abovementioned metrics, we have evaluated our models with the Matthews correlation coefficient (MCC) to produce a more informative and truthful score and to avoid overly optimistic outcomes [52]
Summary
The National Institute on Aging and Alzheimer’s Association (NIA-AA) defines three stages of AD on the basis of pathobiology and clinical symptoms [1]. The stages are a) preclinical AD or asymptomatic predementia (aAD) b) MCI due to AD (mAD) and c) AD dementia (ADD). The brain contains beta-amyloid outside the neuronal cells and tau tangles inside the neurons in different phases of AD [2, 3]. Unlike mAD and ADD, the aAD stage is not associated with cognitive symptoms. In addition to clinical evaluation and psychological tests, artificial intelligence (AI)-based computer-aided diagnosis (CAD) methods for staging AD from structured magnetic.
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