ArticleProceedings of machine learning research2022
TopoFit: Rapid Reconstruction of Topologically-Correct Cortical Surfaces.
Article in Proceedings of machine learning research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- From Low Field to High Value: Robust Cortical Mapping From Low-Field MRI.Human brain mapping · 2026Article
- Preeclampsia as a reversible risk factor for Alzheimer's disease: A prospective MRI study on morphological changes of the cerebral cortex and impairment of cognitive functions.The journal of prevention of Alzheimer's disease · 2026Article
- Unified Brain Surface and Volume Registration.... International Conference on Learning Representations · 2026Article
- DEEP-GYRALNET: ENABLING MACHINE LEARNING IN GYRAL FOLDING PATTERN EXTRACTION ON CORTICAL SURFACE.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- SurfNet: Reconstruction of Cortical Surfaces via Coupled Diffeomorphic Deformations.IEEE transactions on medical imaging · 2025Article
- "Recon-all-clinical": Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI.Medical image analysis · 2025Article
- SurfNet: Reconstruction of Cortical Surfaces via Coupled Diffeomorphic Deformations.bioRxiv : the preprint server for biology · 2025Article
- V2C-Long: Longitudinal cortex reconstruction with spatiotemporal correspondence.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Pseudo-Rendering for Resolution and Topology-Invariant Cortical Parcellation.Machine learning in medical imaging. MLMI (Workshop) · 2025Article
- Synthetic data in generalizable, learning-based neuroimaging.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation.Advances in neural information processing systems · 2023Article
- Systematic cortical thickness and curvature patterns in primates.NeuroImage · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Mesh-based reconstruction of the cerebral cortex is a fundamental component in brain image analysis. Classical, iterative pipelines for cortical modeling are robust but often time-consuming, mostly due to expensive procedures that involve topology correction and spherical mapping. Recent attempts to address reconstruction with machine learning methods have accelerated some components in these pipelines, but these methods still require slow processing steps to enforce topological constraints that comply with known anatomical structure. In this work, we introduce a novel learning-based strategy, TopoFit, which rapidly fits a topologically-correct surface to the white-matter tissue boundary. We design a joint network, employing image and graph convolutions and an efficient symmetric distance loss, to learn to predict accurate deformations that map a template mesh to subject-specific anatomy. This technique encompasses the work of current mesh correction, fine-tuning, and inflation processes and, as a result, offers a 150× faster solution to cortical surface reconstruction compared to traditional approaches. We demonstrate that TopoFit is 1.8× more accurate than the current state-of-the-art deep-learning strategy, and it is robust to common failure modes, such as white-matter tissue hypointensities.
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37220495PMC10201930What OpenQuestion holds
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.