ArticleNeuroImage2019
Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Microbes on the Mind: Multi-modal Neuroimaging Reveals Gut-Brain Axes in Infants.bioRxiv : the preprint server for biology · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Revealing Fine-grained Genetically Informed Cortical Parcellation Maps of Neonates Based on Multi-view Spectral Clustering.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Weakly Supervised Cerebellar Cortical Surface Parcellation with Self-Visual Representation Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Mapping Genetic Topography of Cortical Thickness and Surface Area in Neonatal Brains.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Article
- iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction.Nature protocols · 2023Review
- Gyral peaks: Novel gyral landmarks in developing macaque brains.Human brain mapping · 2022Article
- Spherical Transformer on Cortical Surfaces.Machine learning in medical imaging. MLMI (Workshop) · 2022Article
- Developmental abnormalities of structural covariance networks of cortical thickness and surface area in autistic infants within the first 2 years.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Path Signature Neural Network of Cortical Features for Prediction of Infant Cognitive Scores.IEEE transactions on medical imaging · 2022Article
- Cortical development coupling between surface area and sulcal depth on macaque brains.Brain structure & function · 2022Article
- S3Reg: Superfast Spherical Surface Registration Based on Deep Learning.IEEE transactions on medical imaging · 2021Article
- Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children.Frontiers in computational neuroscience · 2021Article
- Individual identification and individual variability analysis based on cortical folding features in developing infant singletons and twins.Human brain mapping · 2020Article
- Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprinting.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- 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
- 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
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue contrast and dynamic imaging appearance patterns. Thus, it is inevitable to have large amounts of topological errors in the segmented infant brain tissue images, which lead to inaccurately reconstructed cortical surfaces with topological errors. To address this issue, inspired by recent advances in deep learning, we propose an anatomically constrained network for topological correction on infant cortical surfaces. Specifically, in our method, we first locate regions of potential topological defects by leveraging a topology-preserving level set method. Then, we propose an anatomically constrained network to correct those candidate voxels in the located regions. Since infant cortical surfaces often contain large and complex handles or holes, it is difficult to completely correct all errors using one-shot correction. Therefore, we further enroll these two steps into an iterative framework to gradually correct large topological errors. To the best of our knowledge, this is the first work to introduce deep learning approach for topological correction of infant cortical surfaces. We compare our method with the state-of-the-art methods on both simulated topological errors and real topological errors in human infant brain MR images. Moreover, we also validate our method on the infant brain MR images of macaques. All experimental results show the superior performance of the proposed method.
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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.