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Found 12 papers, 6 papers with code
Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging.
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification
Deep learning now enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction or strain.
Ultrafast Cardiac Imaging Using Deep Learning For Speckle-Tracking Echocardiography
The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts.
Phase Unwrapping of Color Doppler Echocardiography using Deep Learning
Color Doppler echocardiography is a widely used non-invasive imaging modality that provides real-time information about the intracardiac blood flow.
Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?
Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by experts (CAMUS being one of the largest public databases).
CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation
For this reason, we propose CRISP a ContRastive Image Segmentation for uncertainty Prediction method.
Echocardiography Segmentation with Enforced Temporal Consistency
In this paper, we propose a framework to learn the 2D+time apical long-axis cardiac shape such that the segmented sequences can benefit from temporal and anatomical consistency constraints.
Cardiac Segmentation with Strong Anatomical Guarantees
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.
LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography
Results obtained on a large open access dataset show that our method outperforms the current best performing deep learning solution and achieved an overall segmentation accuracy lower than the intra-observer variability for the epicardial border (i. e. on average a mean absolute error of 1. 5mm and a Hausdorff distance of 5. 1mm) with 11% of outliers.
Morphological Signature for Improvement of Weakly Supervised Segmentation of Quadriceps Muscles on Magnetic Resonance Imaging Data
Our results show that using few atlases (3 in lieu of 6) based on our morphological measurement improves segmentation quality and decrease computational time for multi-atlas segmentation with CL.
Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis.
Cardiac MRI Segmentation with Strong Anatomical Guarantees
In this paper, we propose a cardiac MRI segmentation method which always produces anatomically plausible results.
