Echocardiography, or cardiac ultrasound, is the most widely used and readily available imaging modality to assess cardiac function and structure. Combining portable instrumentation, rapid image acquisition, high temporal resolution, and without the risks of ionizing radiation, echocardiography is one of the most frequently utilized imaging studies in the United States and serves as the backbone of cardiovascular imaging. For diseases ranging from heart failure to valvular heart diseases, echocardiography is both necessary and sufficient to diagnose many cardiovascular diseases. In addition to our deep learning model, we introduce a new large video dataset of echocardiograms for computer vision research. The EchoNet-Dynamic database includes 10,030 labeled echocardiogram videos and human expert annotations (measurements, tracings, and calculations) to provide a baseline to study cardiac motion and chamber sizes.

EchoNet-Dynamic is a dataset of over 10k echocardiogram, or cardiac ultrasound, videos from unique patients at Stanford University Medical Center. Each apical-4-chamber video is accompanied by an estimated ejection fraction, end-systolic volume, end-diastolic volume, and tracings of the left ventricle performed by an advanced cardiac sonographer and reviewed by an imaging cardiologist.

miyuki17/EchoNet-Dynamic dataset hosted on Hugging Face and contributed by the HF Datasets community

Model comparison. A comparison of DA-UNet++ and other models in terms of parameter quantity and segmentation performance was made, where the abscissa represents the model’s parameter quantity, and the ordinate represents the model’s Dice similarity coefficient on the EchoNet-Dynamic dataset.

This is a collection of synthetic clones for EchoNet-Dynamic (https://echonet.github.io/dynamic/), EchoNet-Pediatric (https://echonet.github.io/pediatric/) and EchoNet-LVH (https://echonet.github.io/lvh/). The datasets are explained in details in https://arxiv.org/abs/2503.22357 If you use this work, please cite it as: @misc{reynaud2025echoflow, title = {EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation}, author = {Hadrien Reynaud and Alberto… See the full description on the dataset page: https://huggingface.co/datasets/HReynaud/EchoFlow.

This paper introduces an optimized nested UNet model for automated left ventricular segmentation in cardiac function assessment. We utilize the EchoNet-Dynamic dataset, which contains both video data and expert annotations. Unlike conventional methods such as DeepLabv3 that struggle with large model sizes and imprecise segmentation, Our proposed model introduces a deeper feature extraction module to effectively capture multi-scale features and reduce computational overhead. By integrating the CBAM (Attention module) attention mechanism and a lightweight SimAM (Simple Attention Module) module, we enhance feature selectivity and minimize redundancy. To further stabilize training and address gradient issues, we combine binary cross-entropy and Dice loss functions. Experimental results reveal that our model significantly outperforms existing methods, achieving a 1.05% increase in the Dice coefficient and reducing model size to 15% of the original. These improvements not only enhance the accuracy of cardiac function assessments but also provide a more efficient solution for automated diagnosis in clinical practice.