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Found 7 papers, 4 papers with code
Stochastic Cortical Self-Reconstruction
Magnetic resonance imaging (MRI) is critical for diagnosing neurodegenerative diseases, yet accurately assessing mild cortical atrophy remains a challenge due to its subtlety.
V2C-Long: Longitudinal Cortex Reconstruction with Spatiotemporal Correspondence
In contrast to existing methods, V2C-Long surfaces are directly comparable in a cross-sectional and longitudinal manner.
Neural deformation fields for template-based reconstruction of cortical surfaces from MRI
The reconstruction of cortical surfaces is a prerequisite for quantitative analyses of the cerebral cortex in magnetic resonance imaging (MRI).
Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning
During inference, similarities of latent features to prototypes are linearly classified to form predictions and attribution maps are provided to explain the similarity.
Abdominal organ segmentation via deep diffeomorphic mesh deformations
However, the generalization of these deep learning-based approaches to different organs and datasets, a crucial property for deployment in clinical environments, has not yet been assessed.
Joint Reconstruction and Parcellation of Cortical Surfaces
The reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative diseases like Alzheimer's disease (AD).
Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks
The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology.
