Pediatric-specific extension of EchoNet-Dynamic: a video-based deep learning model that segments the left ventricle and estimates ejection fraction (EF) from apical-4-chamber (A4C) and parasternal short-axis (PSAX) pediatric echocardiogram clips. Because adult-trained echo models generalize poorly to children (who vary widely in heart size, rate, and image quality), EchoNet-Peds was trained from scratch on a dedicated pediatric video dataset. It segments the LV with a Dice similarity coefficient of 0.89 in both views, estimates EF with a mean absolute error of 3.66%, and identifies pediatric systolic dysfunction with an AUC of 0.95, significantly outperforming an adult-trained model applied to the same pediatric data.

Framework for training an echocardiography left-ventricle segmentation model purely by data-free knowledge distillation: a ConvLSTM-based student network learns to reproduce the masks produced by an EchoNet-Dynamic (DeepLabV3-ResNet50) teacher on entirely synthetic echo videos, with no real labeled data or even real videos required. Achieves state-of-the-art results identifying end-diastolic/end-systolic frames, reaching segmentation quality close to real-data training with substantially fewer weights; also introduces a human-annotation-free evaluation method using a large auxiliary model.

Adapts the Segment Anything Model (SAM) to echocardiography video segmentation by giving it a space-time memory that carries both spatial and temporal cues, so that only the first frame of a video needs an external point prompt and every subsequent frame is segmented from a propagated memory prompt instead. A memory reinforcement mechanism uses each frame's predicted mask to suppress speckle-noise features before they are written back into memory, addressing a key failure mode of naively adapting video object segmentation (e.g. XMem) to noisy ultrasound. Built on SAMUS (an ultrasound-adapted SAM) with a frozen SAM backbone and only the image-encoder adapter layers trained. On the semi-supervised CAMUS and EchoNet-Dynamic benchmarks (only end-diastole/end-systole frames labeled), MemSAM reaches 93.3% and 92.8% mean Dice respectively, outperforming UNet, SwinUNet, H2Former, and prior medical-SAM adaptations (MedSAM, MSA, SAMed, SonoSAM, SAMUS) with far fewer prompts, and derives LVEF (via Simpson's biplane method of disks) with a Pearson correlation of 78.9% against ground truth on CAMUS. Training/inference code is public (MIT license); only the starting SAM ViT-B checkpoint is linked for download, not a separately released fine-tuned MemSAM checkpoint.

Fully automated deep learning workflow for characterizing cardiac mechanics from balanced steady-state free-precession (bSSFP) cine cardiac MRI. It decouples two convolutional networks—a segmentation net (CarSON) and a 3D motion-estimation net (CarMEN)—to derive left- and right-ventricular volumes plus global and regional myocardial strain and strain rate without manual tracing. Trained and validated on healthy and cardiovascular-disease subjects and shown to be robust across MRI vendors, with excellent intra-scanner repeatability for strain. Developed at Massachusetts General Hospital and the Harvard-MIT Division of Health Sciences and Technology.

Foundation model for cine cardiac MRI, self-supervised via masked autoencoding on nearly 75,000 UK Biobank scans. Uses a Vision Transformer with a convolutional stem, unified across long-axis and short-axis views. Fine-tuned checkpoints are released for ventricle and myocardium segmentation, ejection-fraction regression, cardiovascular disease classification, and landmark localization across several public benchmark datasets (ACDC, M&Ms, M&Ms2, EMIDEC, and others).

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.