Cross-modality cardiac image segmentation model that addresses spatial-temporal confounding -- where the anatomy and imaging-modality elements of cardiac images are intertwined across space and time. DCL performs multi-dimensional causal intervention, modeling causal relationships between images and labels as well as causality along the time and space dimensions, integrating historical optimal interventions to transfer knowledge across temporal contexts. A diffusion mechanism further keeps extracted anatomical elements causally invariant across modalities. On cross-modality cardiac images (MR, CT, and ultrasound), DCL achieved a mean Dice of 0.951, outperforming other advanced segmentation methods.

Reinforcement-learning-based unsupervised domain adaptation framework for spatio-temporal (2D+time) echocardiography segmentation, extending the authors' earlier RL4Seg work to full-length video sequences. RL4Seg3D uses a sliding-window approach supporting high-resolution, full-sized inputs, and fuses multiple reward mechanisms to improve segmentation reliability without requiring additional expert annotations in the target domain. Trained and evaluated on a large dataset of over 30,000 echocardiography videos, it outperforms baselines and foundation models on overall segmentation accuracy as well as echocardiography-specific metrics including anatomical/temporal validity and mitral-valve-commissure landmark precision, and supports test-time optimization via calibrated uncertainty estimates.

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.

The original U-Net baseline segmentation network introduced alongside the CAMUS (Cardiac Acquisitions for Multi-structure Ultrasound Segmentation) dataset, one of the largest fully open-access, expert-annotated 2D echocardiography benchmarks. The network segments the left ventricle endocardium (LVEndo), left ventricle epicardium/myocardium (LVEpi), and left atrium (LA) from apical 2-chamber and 4-chamber echo views at end-diastole (ED) and end-systole (ES). In the original ten-fold cross-validation benchmark comparing U-Net, U-Net++, Stacked Hourglass, Anatomically Constrained Neural Networks, and classical methods, the U-Net variant (18M parameters) achieved the best overall accuracy, reaching Dice scores of 0.939 (ED) / 0.916 (ES) for LVEndo and 0.954 (ED) / 0.945 (ES) for LVEpi, approaching inter-observer variability. A pretrained checkpoint of this baseline U-Net is distributed via the University of Sherbrooke's vitalab CASTOR project as part of a broader library for building anatomically-constrained cardiac segmentation pipelines.

Deep learning framework, developed in collaboration with the MONAI community, for automatic segmentation of tricuspid valve leaflets from transthoracic 3D echocardiograms in children with hypoplastic left heart syndrome (HLHS) and other forms of single-ventricle congenital heart disease, integrated into 3D Slicer via MONAILabel for interactive clinical/research use. Addresses a modality (pediatric 3D echocardiography) and population (single-ventricle congenital heart disease) largely absent from adult-focused cardiac AI models.

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.

Self- and weakly-supervised pipeline for left-ventricle segmentation across the full cardiac cycle in apical-4-chamber echocardiography videos. A video segmentation network (2D super-image or 3D U-Net encoder) is first pretrained with a self-supervised temporal-masking objective on largely unannotated echo frames, then fine-tuned with weak supervision from the sparse end-diastole/end-systole frame labels that most echo datasets provide. Achieves 93.3% Dice on EchoNet-Dynamic, outperforming nnU-Net and non-SSL baselines, and generalizes to the external CAMUS dataset. Developed by the BioMedIA group at MBZUAI.

Open-source pipeline that classifies aortic stenosis (AS) severity from transthoracic echocardiography by combining structural and functional information. Video-based R(2+1)D convolutional networks read six B-mode and color Doppler views while a segmentation model measures peak aortic-jet velocity, and an ensemble integrates these into a final severity prediction. Trained on 210,193 images from Kaiser Permanente Northern California and validated across held-out, temporally distinct, and external Stanford and Cedars-Sinai cohorts, reaching AUCs up to 0.96–0.99 for severe AS. Developed by the 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.