Stanford University / Ouyang Lab
End-to-end pipeline for apical-4-chamber echocardiogram videos that segments the left ventricle, estimates ejection fraction on a beat-to-beat basis, and classifies cardiomyopathy with reduced ejection fraction. Combines a DeepLabV3-ResNet50 segmentation model with a 3D CNN (R2+1D/R3D/MC3) initialized on the Kinetics-400 video dataset. Trained on the public EchoNet-Dynamic dataset released alongside it, and one of the most widely reused open echocardiography models since its 2020 Nature publication. Developed by Stanford University.
Architecture
Hybrid
DeepLabV3-ResNet50 for frame-wise LV semantic segmentation + R2+1D/R3D/MC3 3D-CNN (Kinetics-400 initialized) for beat-to-beat ejection-fraction regression
Framework
PyTorch
Added to catalog
2026-07-10
MIT
License for model weights only. Associated code may be licensed seperately, check code source for specific terms.
10,030 deidentified apical-4-chamber echo videos from Stanford Health Care. Source reports age and sex breakdowns.
LVEF (video-level)
Heart failure with reduced EF classification
Segmentation