LV Segmentation
5 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in LV Segmentation
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Echonet-Dynamic
Most implemented papers
A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI
In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic segmentation tool for the LV from short-axis cardiac MRI datasets.
Video-based AI for beat-to-beat assessment of cardiac function
echonet/dynamic
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Nature 2020
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness.
Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound
In particular, segmentations of the left ventricle can be used to derive ventricular volume, ejection fraction (EF) and other relevant measurements.
Cyclical Self-Supervision for Semi-Supervised Ejection Fraction Prediction from Echocardiogram Videos
We also introduce teacher-student distillation to distill the information from LV segmentation masks into an end-to-end LVEF regression model that only requires video inputs.
SimLVSeg: Simplifying Left Ventricular Segmentation in 2D+Time Echocardiograms with Self- and Weakly-Supervised Learning
From calculating biomarkers such as ejection fraction to the probability of a patient's heart failure, accurate segmentation of the heart structures allows doctors to assess the heart's condition and devise treatments with greater precision and accuracy.
