ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2018
A Tetrahedron-based Heat Flux Signature for Cortical Thickness Morphometry Analysis.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- TetCNN: Convolutional Neural Networks on Tetrahedral Meshes.Information processing in medical imaging : proceedings of the ... conference · 2023Article
- Tetrahedral spectral feature-Based bayesian manifold learning for grey matter morphometry: Findings from the Alzheimer's disease neuroimaging initiative.Medical image analysis · 2021Article
- Predicting future cognitive decline with hyperbolic stochastic coding.Medical image analysis · 2021Article
- Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis.IEEE transactions on medical imaging · 2020Article
- Morphometric Gaussian Process for Landmarking on Grey Matter Tetrahedral Models.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- Convolutional Bayesian Models for Anatomical Landmarking on Multi-Dimensional Shapes.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline.NeuroImage. Clinical · 2020Article
- Multi-task Dictionary Learning based on Convolutional Neural Networks for Longitudinal Clinical Score Predictions in Alzheimer's Disease.Human brain and artificial intelligence : first International Workshop, HBAI 2019, held in conjunction with IJCAI 2019, Macao, China, August 12, 2019, revised selected papers. HBAI (Conference) (1st : 2019 : Macau, China) · 2019Article
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Authors and funding
4 authors.
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Abstract
Cortical thickness analysis of brain magnetic resonance images is an important technique in neuroimaging research. There are two main computational paradigms, namely voxel-based and surface-based methods. Recently, a tetrahedron-based volumetric morphometry (TBVM) approach involving proper discretization methods was proposed. The multi-scale and physics-based geometric features generated through such methods may yield stronger statistical power. However, several challenges, such as the lack of well-defined thickness statistics and the difficulty in filling tetrahedrons into the thin and curvy cortex structure, impede the broad application of TBVM. In this paper, we present a universal cortical thickness morphometry analysis approach called
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