# Automated Interpretable 2D Video Extraction from 3D Echocardiography

arXiv: https://arxiv.org/abs/2511.15946 · HTML: https://mlsanity.com/paper/2511.15946/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis](https://mlsanity.com/paper/2401.00153/) (2023)
- [High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning](https://mlsanity.com/paper/2106.12511/) (2021)
- [Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents](https://mlsanity.com/paper/1806.03228/) (2018)
- [EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation](https://mlsanity.com/paper/2410.09704/) (2024)
- [Cardiac Copilot: Automatic Probe Guidance for Echocardiography with World Model](https://mlsanity.com/paper/2406.13165/) (2024)
- [EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance](https://mlsanity.com/paper/2504.13065/) (2025)
- [EchoApex: A General-Purpose Vision Foundation Model for Echocardiography](https://mlsanity.com/paper/2410.11092/) (2024)
- [Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views](https://mlsanity.com/paper/2309.12805/) (2023)

## Built on by (most cited first, 1 of 1)

- [EchoXFlow: A Beamspace Echocardiography Dataset for Cardiac Motion, Flow, and Function](https://mlsanity.com/paper/2605.05447/) (2026)

## Abstract (arXiv)

Although the heart has complex three-dimensional (3D) anatomy, conventional medical imaging with cardiac ultrasound relies on a series of 2D videos showing individual cardiac structures. 3D echocardiography is a developing modality that now offers adequate image quality for clinical use, with potential to streamline acquisition and improve assessment of off-axis features. We propose an automated method to select standard 2D views from 3D cardiac ultrasound volumes, allowing physicians to interpret the data in their usual format while benefiting from the speed and usability of 3D scanning. Applying a deep learning view classifier and downstream heuristics based on anatomical landmarks together with heuristics provided by cardiologists, we reconstruct standard echocardiography views. This approach was validated by three cardiologists in blinded evaluation (96% accuracy in 1,600 videos from 2 hospitals). The downstream 2D videos were also validated in their ability to detect cardiac abnormalities using AI echocardiography models (EchoPrime and PanEcho) as well as ability to generate clinical-grade measurements of cardiac anatomy (EchoNet-Measurement). We demonstrated that the extracted 2D videos preserve spatial calibration and diagnostic features, allowing clinicians to obtain accurate real-world interpretations from 3D volumes. We release the code and a dataset of 29 3D echocardiography videos https://github.com/echonet/3d-echo .