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
LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation
In this study, the performance of existing U-shaped neural network architectures was enhanced for medical image segmentation by adding Transformer.
Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI
This challenge leads to necessitate extensive training data in many deep learning reconstruction methods.
TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82-Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases.
Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation
This paper presents a novel approach that synergies spatial and spectral representations to enhance domain-generalized medical image segmentation.
CaRe-CNN: Cascading Refinement CNN for Myocardial Infarct Segmentation with Microvascular Obstructions
Late gadolinium enhanced (LGE) magnetic resonance (MR) imaging is widely established to assess the viability of myocardial tissue of patients after acute myocardial infarction (MI).
SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects
Our approach has the potential to augment the image-segmentation pairs for rarer CHD types for cardiac segmentation and generate cohorts of CHD cardiac meshes for computational simulation.
Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation
However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i. e., non-i. i. d.)
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation
To achieve efficient few-shot cross-modality segmentation, we propose a novel transformation-consistent meta-hallucination framework, meta-hallucinator, with the goal of learning to diversify data distributions and generate useful examples for enhancing cross-modality performance.
BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability
Specifically, we first decompose an image into a spatial-correlated variable and a spatial-variant variable, assigning hierarchical Bayesian priors to explicitly force them to model the domain-stable shape and domain-specific appearance information respectively.
Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels
In this work, we propose an unsupervised framework for multi-class segmentation with both intensity and shape constraints.
