Self-supervised deep learning model for coronary artery segmentation from invasive X-ray coronary angiography (ICA), designed to reduce reliance on large annotated datasets. CM-UNet combines a Contrastive Masked Autoencoder (CMAE) with a UNet backbone: an online encoder-decoder branch reconstructs masked image patches while a momentum branch produces contrastive embeddings, jointly pretraining the network on unannotated angiography images before fine-tuning on a small labeled set. Fine-tuning with only 18 annotated images (instead of 500) led to just a 15.2% drop in Dice score, versus a 46.5% drop for baseline models trained without this self-supervised pretraining -- demonstrating strong label efficiency for coronary segmentation.

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.

Domain-Shuffle Temporal Attention Network for coronary vessel extraction from X-ray coronary angiography (XCA), trained entirely on synthetic temporal XCA data without requiring manual vessel annotations. By leveraging synthetic data generation and a domain-shuffle temporal attention mechanism, DOSTA-Net avoids the need for costly expert-labeled real angiography sequences while still learning temporally consistent vessel segmentation across frames of an XCA sequence.

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.

Disentangled representation learning model for cardiac image analysis that factorises 2D medical images (MRI, CT) into a spatial 'anatomy factor' (a semantically meaningful multi-channel map, produced by a U-Net-style anatomy encoder) and a non-spatial 'modality factor' (a latent vector capturing imaging-specific characteristics). This disentangled representation supports semi-supervised segmentation using only a fraction of labeled images (matching fully supervised performance), multi-task learning (e.g. jointly regressing cardiac indices), multimodal pooling of MRI and CT data, and image-to-image synthesis between modalities via latent-space arithmetic (swapping modality factors). SDNet also demonstrates that its modality factor alone can predict the input imaging modality with high accuracy.

Automatic coronary artery segmentation pipeline for coronary CT angiography (CCTA). A 2D DenseNet classifier first screens out CT slices that don't contain coronary artery, then a 3D-UNet -- enhanced with dense blocks in the encoder for richer feature extraction and residual, feature-rectifying blocks in the decoder -- segments the coronary artery tree in the remaining slices. A Gaussian-weighted merging scheme combines overlapping 3D patch predictions, up-weighting the more reliable predictions near each patch's center. On the authors' in-house CCTA dataset, the method achieved a Dice similarity coefficient of 0.826.

Open-source, user-guided deep learning tool for coronary artery segmentation from invasive coronary angiography (ICA), designed to improve on traditional quantitative coronary angiography (QCA) edge-detection algorithms that typically require manual correction. Rather than segmenting the whole coronary tree indiscriminately, AngioPy lets the user click a handful of ground-truth points along a specific target vessel (including side branches), and predicts a binary mask for that single artery at the chosen cardiac-cycle time-step. Evaluated against an established QCA system on angiograms from the FAME 2 trial, AngioPy achieved an average F1 score of 0.927 (internal) and 0.924 (external validation), with vessel-diameter and lesion minimal-lumen-diameter measurements showing excellent agreement with QCA (r=0.93-0.96).

Deep learning model for multi-class 3D segmentation of the aorta and its thirteen branches from CT angiography, intended to support planning of endovascular aortic interventions. CIS-UNet combines a CNN encoder with a symmetric decoder and a novel Context-aware Shifted Window Self-Attention (CSW-SA) bottleneck block that adapts the Swin transformer's patch-merging mechanism to more efficiently capture global spatial context. Trained and evaluated via 4-fold cross-validation on the first public multi-branch aorta CTA dataset (59 patients), CIS-UNet outperformed the state-of-the-art SwinUNETR baseline, achieving a mean Dice of 0.713 vs. 0.697 and mean surface distance of 2.78mm vs. 3.39mm, while being more computationally efficient.

Two-stage deep learning pipeline that localizes premature ventricular contraction (PVC) beats directly from raw, unsegmented ECG signal, without relying on hand-crafted features or pre-existing R-peak annotations. An encoder-decoder network first localizes the R-peak of every heartbeat (normal or anomalous); the resulting R-peak positions are then passed to CardioIncNet, a 1D InceptionTime-based classifier, which delineates each beat as healthy or PVC. Evaluated with both single-dataset and cross-dataset protocols across three public ECG databases, the pipeline reached F1 scores above 0.99 (single-dataset) and 0.979 (cross-dataset) for R-peak localization, and above 0.96 and 0.85 respectively for PVC beat classification.

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.

Deep channel-attention network for segmenting the full coronary vessel tree from sequential X-ray coronary angiography (XCA) frames, rather than a single static image. An encoder-decoder architecture fuses temporal-spatial feature maps across the XCA sequence via skip connections, then uses channel-attention blocks in the decoder to refine features and separate thin vessel structures from complex, noisy backgrounds; a Dice loss addresses the severe foreground/background class imbalance typical of XCA. The authors report that SVS-net outperforms prior 2D and video-based baselines on both quantitative vessel-segmentation metrics and visual validation.

Self-supervised learning method for carotid plaque segmentation from B-mode ultrasound images, designed to reduce the amount of expert-labeled data needed to train a segmentation network. A level-set-and-least-squares-based deformation procedure synthesizes registration image pairs from unlabeled carotid ultrasound images, and a spatial-transformer-based registration pretext task pretrains a Stacked U-Net (Su-Net) to focus on plaque-contour features before fine-tuning on a small labeled dataset for total plaque area (TPA) segmentation. Evaluated on carotid ultrasound datasets from two different institutions and countries, the method showed robust generalization when trained with only a small number of labeled images.

Fully automated pipeline for interpreting coronary angiograms that chains four purpose-built neural networks: (1) angiographic projection-angle identification, (2) left/right coronary artery detection, (3) arterial segment localization, and (4) stenosis-severity estimation. Trained on 13,843 angiographic studies (195,195 videos) from 11,972 adult patients at UCSF (2008-2019), with projection-angle and LCA/RCA-detection tasks each reaching precision/sensitivity/F1 at or above 90%. For predicting obstructive coronary artery disease (>=70% stenosis), CathAI reaches an AUC of 0.862 internally, 0.869 on external angiograms from the University of Ottawa Heart Institute, and 0.775 after retraining on quantitative-coronary-angiography labels from the Montreal Heart Institute core lab. No public code or model weights have been released.

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.

Multimodal cardiac MRI foundation model that fuses 3D+T cine CMR (short-axis and long-axis views) with tabular patient health records (demographics, metabolic, and lifestyle factors) from 42,000 UK Biobank participants. Two-stage self-supervised pretraining -- masked-image reconstruction, then imaging-tabular contrastive alignment -- produces representations that transfer to whole-heart segmentation, cardiac phenotype/physiological-feature regression, and cardiac/metabolic disease classification within one unified framework.

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.

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.

Ensemble of ten self-configuring nnU-Net models (five 2D, five 3D) that segments the left ventricle, right ventricle, and myocardium from short-axis cardiac cine MRI. Won the 2020 M&Ms challenge, a multi-centre, multi-vendor, multi-disease benchmark spanning scanners from four vendors and three countries, demonstrating strong generalization across acquisition protocols. Developed by DKFZ, the group behind the widely used nnU-Net framework.

Segments coronary vessels from invasive X-ray angiography images and automatically quantifies the degree of stenosis along the extracted centerlines. Combines MedSAM, a Segment-Anything-style vision model, with a Mamba-based VM-UNet segmentation branch for efficient long-range feature modeling. Trained and evaluated on the ARCADE, DCA1, and GH angiography datasets by researchers at Ocean University of China and Shandong University.