ArticleMedical image analysis2019
Hierarchical spherical deformation for cortical surface registration.
Article in Medical image analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Long-term effects of preterm birth on cortical folding trajectories in early childhood.Brain communications · 2026Article
- Leveraging Input-Level Feature Deformation With Guided-Attention for Sulcal Labeling.IEEE transactions on medical imaging · 2025Article
- Local gyrification index and sulcal depth as imaging markers of cognitive decline in Alzheimer's disease.Frontiers in aging neuroscience · 2025Article
- JOSA: Joint surface-based registration and atlas construction of brain geometry and function.Medical image analysis · 2024Article
- Personalized Patch-based Normality Assessment of Brain Atrophy in Alzheimer's Disease.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Surface-guided computing to analyze subcellular morphology and membrane-associated signals in 3D.bioRxiv : the preprint server for biology · 2023Article
- Surface-guided computing to analyze subcellular morphology and membrane-associated signals in 3D.ArXiv · 2023Article
- Comprehensive shape analysis of the cortex in Huntington's disease.Human brain mapping · 2023Article
- Article
- Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection.Computers in biology and medicine · 2023Article
- Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis.Journal of digital imaging · 2022Article
- Incomplete Hippocampal Inversion: A Neurodevelopmental Mechanism for Hippocampal Shape Deformation in Schizophrenia.Biological psychiatry · 2022Article
- Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- S3Reg: Superfast Spherical Surface Registration Based on Deep Learning.IEEE transactions on medical imaging · 2021Article
- Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training.NeuroImage · 2021Article
- Validation of Group-wise Registration for Surface-based Functional MRI Analysis.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Establishing Surface Correspondence for Post-surgical Cortical Thickness Changes in Temporal Lobe Epilepsy.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Joint analysis of structural connectivity and cortical surface features: correlates with mild traumatic brain injury.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Joint cortical surface and structural connectivity analysis of Alzheimer's Disease.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- AUTOMATIC LABELING OF CORTICAL SULCI USING SPHERICAL CONVOLUTIONAL NEURAL NETWORKS IN A DEVELOPMENTAL COHORT.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
We present hierarchical spherical deformation for a group-wise shape correspondence to address template selection bias and to minimize registration distortion. In this work, we aim at a continuous and smooth deformation field to guide accurate cortical surface registration. In conventional spherical registration methods, a global rigid alignment and local deformation are independently performed. Motivated by the composition of precession and intrinsic rotation, we simultaneously optimize global rigid rotation and non-rigid local deformation by utilizing spherical harmonics interpolation of local composite rotations in a single framework. To this end, we indirectly encode local displacements by such local composite rotations as functions of spherical locations. Furthermore, we introduce an additional regularization term to the spherical deformation, which maximizes its rigidity while reducing registration distortion. To improve surface registration performance, we employ the second order approximation of the energy function that enables fast convergence of the optimization. In the experiments, we validate our method on healthy normal subjects with manual cortical surface parcellation in registration accuracy and distortion. We show an improved shape correspondence with high accuracy in cortical surface parcellation and significantly low registration distortion in surface area and edge length. In addition to validation, we discuss parameter tuning, optimization, and implementation design with potential acceleration.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.