Heart Segmentation
11 papers with code • 1 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Heart Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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MM-WHS 2017
ACDC Scribbles
Echonet-Dynamic
CheXmask
Most implemented papers
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.
A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers
In this work, we propose a simple framework to achieve fair image segmentation of multiple modalities using a single conditional model that adapts its normalization layers based on the input type, trained with non-registered interleaved mixed data.
Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2D Face Pose Estimation and Heart Segmentation in 3D CT Images
We empirically show that global training with BP outperforms layer-wise (pre-)training.
Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks
Nogimon/FlyNet
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Convolutional neural networks are powerful tools for image segmentation and classification.
CFUN: Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation
Wuziyi616/CFUN
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In this paper, we propose a novel heart segmentation pipeline Combining Faster R-CNN and U-net Network (CFUN).
Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
Three-dimensional medical image segmentation is one of the most important problems in medical image analysis and plays a key role in downstream diagnosis and treatment.
Heart Segmentation From MRI Scans Using Convolutional Neural Network
Heart is one of the vital organs of human body.
MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation
Current deep-learning-based registration algorithms often exploit intensity-based similarity measures as the loss function, where dense correspondence between a pair of moving and fixed images is optimized through backpropagation during training.
nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
kent0n-li/medical-image-segmentation
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To combine the strengths of foundational and domain-specific models, we propose nnSAM, integrating SAM's robust feature extraction with nnUNet's automatic configuration to enhance segmentation accuracy on small datasets.
FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation
Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised Domain Adaptation (UDA) is appealing where only unlabeled images are needed for the adaptation.
