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Found 5 papers, 3 papers with code
A Deep Generative Model of Neonatal Cortical Surface Development
The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes.
CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development.
Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces
Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice.
Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis
Motivated by the success of attention-modelling in computer vision, we translate convolution-free vision transformer approaches to surface data, to introduce a domain-agnostic architecture to study any surface data projected onto a spherical manifold.
A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling
We developed an automated system based on deep neural networks for fast and sensitive 3D image segmentation of cortical gray matter from fetal brain MRI.
