Cardiac Segmentation
33 papers with code • 0 benchmarks • 3 datasets
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Libraries
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CAMUS
ACDC Scribbles
Echonet-Dynamic
Latest papers with no code
Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts
Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues.
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method
Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns.
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation
This combination of global and local alignment can precisely localize the crucial regions in segmentation target while preserving the overall semantic consistency.
Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images
Segmentation of the left ventricle in cardiac magnetic resonance imaging MRI scans enables cardiologists to calculate the volume of the left ventricle and subsequently its ejection fraction.
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics
Here, we propose a method for continual active learning operating on a stream of medical images in a multi-scanner setting.
Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks
Successful application of deep architectures for rat cardiac segmentation, although of critical importance for preclinical evaluation of cardiac function, has to our knowledge not yet been reported.
Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation
In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures.
Trilateral Attention Network for Real-time Medical Image Segmentation
The performance of the segmentation stage highly relies on the extracted set of spatial features and the receptive fields.
Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation
Unsupervised domain adaptation is useful in medical image segmentation.
Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions
Cardiac segmentation of atriums, ventricles, and myocardium in computed tomography (CT) images is an important first-line task for presymptomatic cardiovascular disease diagnosis.
