Tetrahedral spectral feature-Based bayesian manifold learning for grey matter morphometry: Findings from the Alzheimer’s disease neuroimaging initiative

• Published in: Medical image analysis Vol. 72; p. 102123
• Main Authors: Fan, Yonghui, Wang, Gang, Dong, Qunxi, Liu, Yuxiang, Leporé, Natasha, Wang, Yalin
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.08.2021; Elsevier BV
• Subjects: Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Bayes Theorem; Bayesian analysis; Bayesian manifold learning; Brain - diagnostic imaging; Cognitive Dysfunction; Feature extraction; Gaussian process; Gray Matter - diagnostic imaging; Humans; Image analysis; Image processing; Image reconstruction; Learning; Machine learning; Magnetic Resonance Imaging; Magnetic resonance imaging (MRI); Manifolds (mathematics); Mathematical models; Medical imaging; Medical research; Morphometry; Neurodegenerative diseases; Neuroimaging; Operators (mathematics); Spectra; Spectral shape analysis; Statistical analysis; Substantia grisea; Tetrahedral mesh; Thermodynamics; Magnetic resonance imaging (MRI); Tetrahedral mesh; Spectral shape analysis; Alzheimer’S disease; Bayesian manifold learning
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2021.102123

•A systematic cortical morphometry analysis on three-dimensional manifold.•Explicit weak-form formulations for Laplace-Beltrami and Hamiltonian operators.•Solving heat equation and Schrȵdinger’s equation for tetrahedral spectral analysis.•A novel morphometric Gaussian process regression framework for landmarking.•A series of AD diagnosis experiments and visualizations in the ADNI cohort. [Display omitted] Structural and anatomical analyses of magnetic resonance imaging (MRI) data often require a reconstruction of the three-dimensional anatomy to a statistical shape model. Our prior work demonstrated the usefulness of tetrahedral spectral features for grey matter morphometry. However, most of the current methods provide a large number of descriptive shape features, but lack an unsupervised scheme to automatically extract a concise set of features with clear biological interpretations and that also carries strong statistical power. Here we introduce a new tetrahedral spectral feature-based Bayesian manifold learning framework for effective statistical analysis of grey matter morphology. We start by solving the technical issue of generating tetrahedral meshes which preserve the details of the grey matter geometry. We then derive explicit weak-form tetrahedral discretizations of the Hamiltonian operator (HO) and the Laplace-Beltrami operator (LBO). Next, the Schrödinger’s equation is solved for constructing the scale-invariant wave kernel signature (SIWKS) as the shape descriptor. By solving the heat equation and utilizing the SIWKS, we design a morphometric Gaussian process (M-GP) regression framework and an active learning strategy to select landmarks as concrete shape descriptors. We evaluate the proposed system on publicly available data from the Alzheimers Disease Neuroimaging Initiative (ADNI), using subjects structural MRI covering the range from cognitively unimpaired (CU) to full blown Alzheimer’s disease (AD). Our analyses suggest that the SIWKS and M-GP compare favorably with seven other baseline algorithms to obtain grey matter morphometry-based diagnoses. Our work may inspire more tetrahedral spectral feature-based Bayesian learning research in medical image analysis.