ArticleNeuroImage2020
Cortical surface registration using unsupervised learning.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- DEEP-LEARNING CORTICAL REGISTRATION GUIDED BY STRUCTURAL AND DIFFUSION MRI AND CONNECTIVITY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- DEEP-LEARNING CORTICAL REGISTRATION GUIDED BY STRUCTURAL AND DIFFUSION MRI AND CONNECTIVITY.bioRxiv : the preprint server for biology · 2026Article
- Unified Brain Surface and Volume Registration.... International Conference on Learning Representations · 2026Article
- Subcortical Hubs of Brain Networks Sustaining Human Consciousness.Human brain mapping · 2025Article
- Shared subcortical arousal systems across sensory modalities during transient modulation of attention.NeuroImage · 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
- Shared subcortical arousal systems across sensory modalities during transient modulation of attention.bioRxiv : the preprint server for biology · 2024Article
- Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning.Medical image analysis · 2024Article
- Intrauterine exposure to chorioamnionitis and neuroanatomical alterations at term-equivalent age in preterm infants.Archives of gynecology and obstetrics · 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
- Article
- Computing personalized brain functional networks from fMRI using self-supervised deep learning.Medical image analysis · 2023Article
- Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection.Computers in biology and medicine · 2023Article
- Machine learning in neuroimaging: from research to clinical practice.Radiologie (Heidelberg, Germany) · 2022Review
- Fast cortical surface reconstruction from MRI using deep learning.Brain informatics · 2022Article
- Article
- S3Reg: Superfast Spherical Surface Registration Based on Deep Learning.IEEE transactions on medical imaging · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment, but often requires high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph.
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What 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.