ArticleIEEE transactions on medical imaging2021
S3Reg: Superfast Spherical Surface Registration Based on Deep Learning.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Unsupervised multimodal surface registration with geometric deep learning.Medical image analysis · 2026Article
- Article
- Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data.... International Conference on Learning Representations · 2025Article
- UNSUPERVISED CORTICAL SURFACE REGISTRATION NETWORK FOR ALIGNING GYRALNET.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- JOSA: Joint surface-based registration and atlas construction of brain geometry and function.Medical image analysis · 2024Article
- Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning.Medical image analysis · 2024Article
- Article
- 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
- Prognostic potential of a voxelwise invasion risk map of nasopharyngeal carcinoma based on a coordinate system of the nasopharynx.Quantitative imaging in medicine and surgery · 2023Article
- Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection.Computers in biology and medicine · 2023Article
- Fast Spherical Mapping of Cortical Surface Meshes Using Deep Unsupervised Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- TopoFit: Rapid Reconstruction of Topologically-Correct Cortical Surfaces.Proceedings of machine learning research · 2022Article
- ABCnet: Adversarial bias correction network for infant brain MR images.Medical image analysis · 2021Article
- A Deep Network for Joint Registration and Parcellation of Cortical Surfaces.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionArticle
- Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionArticle
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Authors and funding
8 authors.
Funding
Abstract
Cortical surface registration is an essential step and prerequisite for surface-based neuroimaging analysis. It aligns cortical surfaces across individuals and time points to establish cross-sectional and longitudinal cortical correspondences to facilitate neuroimaging studies. Though achieving good performance, available methods are either time consuming or not flexible to extend to multiple or high dimensional features. Considering the explosive availability of large-scale and multimodal brain MRI data, fast surface registration methods that can flexibly handle multimodal features are desired. In this study, we develop a Superfast Spherical Surface Registration (S3Reg) framework for the cerebral cortex. Leveraging an end-to-end unsupervised learning strategy, S3Reg offers great flexibility in the choice of input feature sets and output similarity measures for registration, and meanwhile reduces the registration time significantly. Specifically, we exploit the powerful learning capability of spherical Convolutional Neural Network (CNN) to directly learn the deformation fields in spherical space and implement diffeomorphic design with "scaling and squaring" layers to guarantee topology-preserving deformations. To handle the polar-distortion issue, we construct a novel spherical CNN model using three orthogonal Spherical U-Nets. Experiments are performed on two different datasets to align both adult and infant multimodal cortical features. Results demonstrate that our S3Reg shows superior or comparable performance with state-of-the-art methods, while improving the registration time from 1 min to 10 sec.
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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.