ArticleIEEE transactions on medical imaging2021
Spherical Deformable U-Net: Application to Cortical Surface Parcellation and Development Prediction.
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 33 papers.
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Who cites it
33 citing papers in PubMed.
- Feynman-Kac Reweighted Schrödinger Bridge Matching for Surface-based Tau PET Harmonization.ArXiv · 2026Article
- Surface-based brain age index reflects hematological impacts on cognitive development in children with beta-thalassemia major.iScience · 2026Article
- Estimating the fraction of variance of crystallized intelligence explained by cortical surface area in early adolescence.bioRxiv : the preprint server for biology · 2026Article
- Early life phthalate and replacement plasticizer exposures and changes in early childhood brain functional connectivity and structural morphology.Environment international · 2026Article
- Unified Brain Surface and Volume Registration.... International Conference on Learning Representations · 2026Article
- Anatomy-Guided Surface Diffusion Model for Alzheimer's Disease Normative Modeling.Proceedings of machine learning research · 2025Article
- Article
- Flexible Individualized Developmental Prediction of Infant Cortical Surface Maps via Intensive Triplet Autoencoder.IEEE transactions on medical imaging · 2025Article
- Application of improved graph convolutional network for cortical surface parcellation.Scientific reports · 2025Article
- TAU PET HARMONIZATION VIA SURFACE-BASED DIFFUSION MODEL.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Semantic-spatial feature-fused cortical surface parcellation: a scale-unified spatial learning network with boundary contrastive loss.Medical & biological engineering & computing · 2025Article
- UNSUPERVISED CORTICAL SURFACE REGISTRATION NETWORK FOR ALIGNING GYRALNET.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Leveraging Input-Level Feature Deformation With Guided-Attention for Sulcal Labeling.IEEE transactions on medical imaging · 2025Article
- JOSA: Joint surface-based registration and atlas construction of brain geometry and function.Medical image analysis · 2024Article
- Longitudinally Consistent Individualized Prediction of Infant Cortical Morphological Development.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Consecutive-Contrastive Spherical U-Net: Enhancing Reliability of Individualized Functional Brain Parcellation for Short-Duration fMRI Scans.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Disentangled Hybrid Transformer for Identification of Infants with Prenatal Drug Exposure.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning.Medical image analysis · 2024Article
- A joint parcellation and boundary network with multi-rate-shared dilated graph attention for cortical surface parcellation.Medical & biological engineering & computing · 2024Article
- Automatic cortical surface parcellation in the fetal brain using attention-gated spherical U-net.Frontiers in neuroscience · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Convolutional Neural Networks (CNNs) have achieved overwhelming success in learning-related problems for 2D/3D images in the Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have an inherent spherical topology in a manifold space, e.g., the convoluted brain cortical surfaces represented by triangular meshes. There is no consistent neighborhood definition and thus no straightforward convolution/pooling operations for such cortical surface data. In this paper, leveraging the regular and hierarchical geometric structure of the resampled spherical cortical surfaces, we create the 1-ring filter on spherical cortical triangular meshes and accordingly develop convolution/pooling operations for constructing Spherical U-Net for cortical surface data. However, the regular nature of the 1-ring filter makes it inherently limited to model fixed geometric transformations. To further enhance the transformation modeling capability of Spherical U-Net, we introduce the deformable convolution and deformable pooling to cortical surface data and accordingly propose the Spherical Deformable U-Net (SDU-Net). Specifically, spherical offsets are learned to freely deform the 1-ring filter on the sphere to adaptively localize cortical structures with different sizes and shapes. We then apply the SDU-Net to two challenging and scientifically important tasks in neuroimaging: cortical surface parcellation and cortical attribute map prediction. Both applications validate the competitive performance of our approach in accuracy and computational efficiency in comparison with state-of-the-art methods.
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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.