Predict Alzheimer’s disease using hippocampus MRI data: a lightweight 3D deep convolutional network model with visual and global shape representations

Abstract

Background Alzheimer’s disease (AD) is a progressive and irreversible brain disorder. Hippocampus is one of the involved regions and its atrophy is a widely used biomarker for AD diagnosis. We have recently developed DenseCNN, a lightweight 3D deep convolutional network model, for AD classification based on hippocampus magnetic resonance imaging (MRI) segments. In addition to the visual features of the hippocampus segments, the global shape representations of the hippocampus are also important for AD diagnosis. In this study, we propose DenseCNN2, a deep convolutional network model for AD classification by incorporating global shape representations along with hippocampus segmentations. Methods The data was obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and was T1-weighted structural MRI from initial screening or baseline, including ADNI 1,2/GO and 3. DenseCNN2 was trained and evaluated with 326 AD subjects and 607 CN hippocampus MRI using 5-fold cross-validation strategy. DenseCNN2 was compared with other state-of-the-art machine learning approaches for the task of AD classification. Results We showed that DenseCNN2 with combined visual and global shape features performed better than deep learning models with visual or global shape features alone. DenseCNN2 achieved an average accuracy of 0.925, sensitivity of 0.882, specificity of 0.949, and area under curve (AUC) of 0.978, which are better than or comparable to the state-of-the-art methods in AD classification. Data visualization analysis through 2D embedding of UMAP confirmed that global shape features improved class discrimination between AD and normal. Conclusion DenseCNN2, a lightweight 3D deep convolutional network model based on combined hippocampus segmentations and global shape features, achieved high performance and has potential as an efficient diagnostic tool for AD classification.

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

“…Data type AUC [51] sMRI 0.8722 [52] sMRI 0.861 [53] sMRI 0.76 [57] SNPs (482) 0.842 [16] SNPs (2500) 0.719 [58] SNPs (11) 0.8949 MRI 0.993 [59] sMRI 0.9252 [60] amyloid PET 0.908 [61] amyloid PET + MRI 0.9234 [63] SNPs (200) 0.62 [17] SNPs (20 & 50) 0.68 [18] SNPs (41) 0.6807 [19] SNPs (20) 0.689 *Note: images data results were measured by ACC journal.ump.edu.my/ijsecs ◄ Furthermore, when the results of ML and DL based approaches were compared in terms of genetic variants data and neuroimaging data, shown Table 3, it was found that DL based approaches had achieved better performance in Neuroimaging data compared to ML based approaches, while they were relatively poor with SNPs data.…”

“…The model attained a testing accuracy of 99.30. Another approach exploited MRI data in [59] to predict AD. Researchers used structural MRI features extracted from the hippocampus area of 933 subjects.…”

Alzheimer’s Disease Classification Using Cluster-based Labelling for Graph Neural Network on Heterogeneous Data

“…amyloid PET, and the recent approval of its use by the U.S. FDA (Jie et al, 2021). Importantly, these studies, together with the literature on unsupervised learning approaches applied to AD (Gamberger et al, 2016; Gamberger et al, 2017; Martí-Juan et al, 2019; Ferreira et al, 2019; Alashwal et al, 2019; Wang et al, 2021; Katabathula et al, 2021; Prakash et al, 2021, Alexander et al, 2020; Alexander et al, 2021; Prakash et al, 2021), have not used cluster-based class re-labelling for GNN’s classification of AD. Here, we have made use of the robust auto-metric GNN model by Song et al (2021).…”

“…Complementing the supervised learning of GNN, unsupervised learning such as dimensional reduction, clustering and data visualisation have been used for providing further insights into heterogeneous AD data (e.g. Alashwal et al, 2019; Wang et al, 2021; Katabathula et al, 2021). In particular, some studies using clustering methods had identified sub-populations that were relatively homogeneous based on clinical and biological features (Gamberger et al, 2016; Martí-Juan et al, 2019; Ferreira et al, 2019; Prakash et al, 2021, Alexander et al, 2020; Alexander et al, 2021), differing rates of cognitive decline of sub-groups of prodromal AD patients (Gamberger et al, 2017; Alexander et al, 2020; Alexander et al, 2021), and for stratifying treatments (Prakash et al, 2021).…”

Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics

“…Deep learning-based methods have recently gained great momentum in both image reconstruction ( 128 ) and postprocessing ( 129 , 130 ). Here, we focus on the DL application in image postprocessing with emphasis on image segmentation in a mono-modality and registration between different modalities.…”