Cardiac Segmentation
33 papers with code • 0 benchmarks • 3 datasets
Benchmarks
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Libraries
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CAMUS
ACDC Scribbles
Echonet-Dynamic
Most implemented papers
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.
Cardiac MRI Segmentation with Strong Anatomical Guarantees
vitalab/castor
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In this paper, we propose a cardiac MRI segmentation method which always produces anatomically plausible results.
An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image
A CNN segmentation model was trained based on the augmented training data by leave-one-out strategy.
L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI
SMKamrulHasan/Learned-Condensation-Optimizaton-Network
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In this work, we implement a fully convolutional segmenter featuring both a learned group structure and a regularized weight-pruner to reduce the high computational cost in volumetric image segmentation.
Cardiac Segmentation with Strong Anatomical Guarantees
vitalab/castor
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In this paper, we present a framework for producing cardiac image segmentation maps that are guaranteed to respect pre-defined anatomical criteria, while remaining within the inter-expert variability.
Learning Directional Feature Maps for Cardiac MRI Segmentation
Cardiac MRI segmentation plays a crucial role in clinical diagnosis for evaluating personalized cardiac performance parameters.
Disentangled Representations for Domain-generalized Cardiac Segmentation
Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains.
Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation
In this way, the dual teacher models would transfer acquired inter- and intra-domain knowledge to the student model for further integration and exploitation.
Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation
tfboys-lzz/mpscl
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To bridge the gap between the source and target domains in unsupervised domain adaptation (UDA), the most common strategy puts focus on matching the marginal distributions in the feature space through adversarial learning.
Source-Free Domain Adaptation for Image Segmentation
mathilde-b/SFDA
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Our method yields comparable results to several state of the art adaptation techniques, despite having access to much less information, as the source images are entirely absent in our adaptation phase.
