ArticleInformation processing in medical imaging : proceedings of the ... conference2019
Spherical U-Net on Cortical Surfaces: Methods and Applications.
Article in Information processing in medical imaging : proceedings of the ... conference, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
32 citing papers in PubMed, 73 citations in OpenAlex.
- Estimating the fraction of variance of crystallized intelligence explained by cortical surface area in early adolescence.bioRxiv : the preprint server for biology · 2026Article
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- Unsupervised multimodal surface registration with geometric deep learning.Medical image analysis · 2026Article
- Unified Brain Surface and Volume Registration.... International Conference on Learning Representations · 2026Article
- Article
- Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data.... International Conference on Learning Representations · 2025Article
- Leveraging Input-Level Feature Deformation With Guided-Attention for Sulcal Labeling.IEEE transactions on medical imaging · 2025Article
- Attention Rings for Shape Analysis and Application to MRI Quality Control.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- 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
- The Multiscale Surface Vision Transformer.ArXiv · 2024Article
- ShapeAXI: Shape Analysis Explainability and Interpretability.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- IcoConv : Explainable brain cortical surface analysis for ASD classification.Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.) · 2023Article
- Multi-task Joint Prediction of Infant Cortical Morphological and Cognitive Development.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Bidirectional prediction of facial and bony shapes for orthognathic surgical planning.Medical image analysis · 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
- Spherical Transformer on Cortical Surfaces.Machine learning in medical imaging. MLMI (Workshop) · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
Convolutional Neural Networks (CNNs) have been providing the state-of-the-art performance for learning-related problems involving 2D/3D images in Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have a spherical topology in a manifold space, e.g., brain cortical or subcortical surfaces represented by triangular meshes, with large inter-subject and intra-subject variations in vertex number and local connectivity. Hence, there is no consistent neighborhood definition and thus no straightforward convolution/transposed convolution operations for cortical/subcortical surface data. In this paper, by leveraging the regular and consistent geometric structure of the resampled cortical surface mapped onto the spherical space, we propose a novel convolution filter analogous to the standard convolution on the image grid. Accordingly, we develop corresponding operations for convolution, pooling, and transposed convolution for spherical surface data and thus construct spherical CNNs. Specifically, we propose the Spherical U-Net architecture by replacing all operations in the standard U-Net with their spherical operation counterparts. We then apply the Spherical U-Net to two challenging and neuroscientifically important tasks in infant brains: cortical surface parcellation and cortical attribute map development prediction. Both applications demonstrate the competitive performance in the accuracy, computational efficiency, and effectiveness of our proposed Spherical U-Net, in comparison with the state-of-the-art methods.
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Registered trials
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.