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Found 6 papers, 2 papers with code
Framework to generate perfusion map from CT and CTA images in patients with acute ischemic stroke: A longitudinal and cross-sectional study
Stroke is a leading cause of disability and death.
A 3D generative model of pathological multi-modal MR images and segmentations
The proposed joint imaging-segmentation generative model is shown to generate high-fidelity synthetic images and associated segmentations, with the ability to combine pathologies.
Can segmentation models be trained with fully synthetically generated data?
Data augmentation is often used to artificially increase the variability in the data distribution and improve model generalisability.
Morphology-preserving Autoregressive 3D Generative Modelling of the Brain
Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations.
Acquisition-invariant brain MRI segmentation with informative uncertainties
Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and therefore any downstream analyses.
The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties
Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases.
