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Found 7 papers, 0 papers with code
Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusion
This paper proposes a new unsupervised zero-shot-learning method named Mutual Information guided Diffusion cross-modality data translation Model (MIDiffusion), which learns to translate the unseen source data to the target domain.
Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers
In the meantime, recent studies show that the attention-based model (e. g., Transformer) can bring superior performance in pattern recognition tasks.
Cardiac Motion Modeling with Parallel Transport and Shape Splines
In cases of pressure or volume overload, probing cardiac function may be difficult because of the interactions between shape and deformations. In this work, we use the LDDMM framework and parallel transport to estimate and reorient deformations of the right ventricle.
Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets
To tackle this problem we introduce a probabilistic framework for joint cardiac data imputation and personalisation of cardiovascular mechanistic models, with application to brain studies with incomplete heart data.
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss
In this preliminary study, we propose automated, two-stage, three-dimensional U-Nets with convolutional neural network, for the challenging task of left atrial segmentation.
