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Found 11 papers, 8 papers with code
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.
HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery
The wide range of research in deep learning-based medical image segmentation pushed the boundaries in a multitude of applications.
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.
STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains
We propose to predict the uncertainty of pseudo labels and integrate it in the training process with an uncertainty-guided loss function to highlight labels with high certainty.
Importance Driven Continual Learning for Segmentation Across Domains
The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications.
Recalibrating 3D ConvNets with Project & Excite
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging.
`Project & Excite' Modules for Segmentation of Volumetric Medical Scans
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.
