Cardiac Segmentation
29 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Cardiac Segmentation
CAMUS
ACDC Scribbles
Echonet-Dynamic
Most implemented papers
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis.
A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI
vuptran/cardiac-segmentation
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To our knowledge, this is the first application of a fully convolutional neural network architecture for pixel-wise labeling in cardiac magnetic resonance imaging.
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
cheng-01037/Self-supervised-Fewshot-Medical-Image-Segmentation
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ECCV 2020
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation
carrenD/Med-CMDA
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In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e. g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner.
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
vios-s/multimodal_segmentation
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Core to our method is learning a disentangled decomposition into anatomical and imaging factors.
Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation
robustml-eurecom/quality_control_CMR
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Deep learning methods have reached state-of-the-art performance in cardiac image segmentation.
3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation
julien-zheng/CardiacSegmentationPropagation
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We propose a method based on deep learning to perform cardiac segmentation on short axis MRI image stacks iteratively from the top slice (around the base) to the bottom slice (around the apex).
Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences
cq615/Joint-Motion-Estimation-and-Segmentation
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Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases.
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
julien-zheng/CardiacMotionFlow
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We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart.
