Cardiac Segmentation
33 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Cardiac Segmentation
Libraries
Use these libraries to find Cardiac Segmentation models and implementations
CAMUS
ACDC Scribbles
Echonet-Dynamic
Latest papers
Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation
Medical image segmentation is increasingly reliant on deep learning techniques, yet the promising performance often come with high annotation costs.
Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation
Medical image segmentation is essential in diagnostics, treatment planning, and healthcare, with deep learning offering promising advancements.
Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation
Mamba-UNet adopts a pure Visual Mamba (VMamba)-based encoder-decoder structure, infused with skip connections to preserve spatial information across different scales of the network.
Towards Robust Cardiac Segmentation using Graph Convolutional Networks
gillesvntnu/gcn_multistructure
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We propose a graph architecture that uses two convolutional rings based on cardiac anatomy and show that this eliminates anatomical incorrect multi-structure segmentations on the publicly available CAMUS dataset.
NISF: Neural Implicit Segmentation Functions
niloide/implicit_segmentation
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Approaches that rely on convolutional neural networks (CNNs) are limited to grid-like inputs and not easily applicable to sparse or partial measurements.
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization
endoluminalsurgicalvision-imr/acnorm
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Driven by the latest trend towards self-supervised learning (SSL), the paradigm of "pretraining-then-finetuning" has been extensively explored to enhance the performance of clinical applications with limited annotations.
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation
david-stojanovski/echo_from_noise
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We propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps.
Self-Supervised Pretraining for 2D Medical Image Segmentation
In this paper, we elaborate and analyse the effectiveness of supervised and self-supervised pretraining approaches on downstream medical image segmentation, focusing on convergence and data efficiency.
ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation
bwgzk/shapepu
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To tackle this problem, we propose a new scribble-guided method for cardiac segmentation, based on the Positive-Unlabeled (PU) learning framework and global consistency regularization, and termed as ShapePU.
Test-Time Adaptation with Shape Moments for Image Segmentation
mathilde-b/tta
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In typical clinical settings, the source data is inaccessible and the target distribution is represented with a handful of samples: adaptation can only happen at test time on a few or even a single subject(s).
