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Found 6 papers, 1 papers with code
Learning Dense Correspondence from Synthetic Environments
Results show that training 2D-3D mapping network models on synthetic data is a viable alternative to using real data.
CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction
In this paper, we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object.
MongeNet: Efficient Sampler for Geometric Deep Learning
Recent advances in geometric deep-learning introduce complex computational challenges for evaluating the distance between meshes.
DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction
Having these limitations in mind, we propose DeepCSR, a 3D deep learning framework for cortical surface reconstruction from MRI.
Going deeper with brain morphometry using neural networks
Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases.
A Multiple Decoder CNN for Inverse Consistent 3D Image Registration
The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy when compared to their traditional counterparts.
