# EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography

arXiv: https://arxiv.org/abs/2209.04242 · HTML: https://mlsanity.com/paper/2209.04242/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 9 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning](https://mlsanity.com/paper/2201.04676/) (2022)
- [Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation](https://mlsanity.com/paper/2107.00977/) (2021)
- [Contrastive Pretraining for Echocardiography Segmentation with Limited Data](https://mlsanity.com/paper/2201.07219/) (2022)
- [Visual Transformer with Statistical Test for COVID-19 Classification](https://mlsanity.com/paper/2107.05334/) (2021)

## Built on by (most cited first, 16 of 16)

- [EchoApex: A General-Purpose Vision Foundation Model for Echocardiography](https://mlsanity.com/paper/2410.11092/) (2024)
- [CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis](https://mlsanity.com/paper/2403.10164/) (2024)
- [SupReMix: Supervised Contrastive Learning for Medical Imaging Regression with Mixup](https://mlsanity.com/paper/2309.16633/) (2023)
- [EchoNarrator: Generating natural text explanations for ejection fraction predictions](https://mlsanity.com/paper/2410.23744/) (2024)
- [ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography](https://mlsanity.com/paper/2512.03339/) (2025)
- [SimLVSeg: Simplifying Left Ventricular Segmentation in 2D+Time Echocardiograms with Self- and Weakly-Supervised Learning](https://mlsanity.com/paper/2310.00454/) (2023)
- [EchoDFKD: Data-Free Knowledge Distillation for Cardiac Ultrasound Segmentation using Synthetic Data](https://mlsanity.com/paper/2409.07566/) (2024)
- [Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression](https://mlsanity.com/paper/2503.04131/) (2025)
- [ControlEchoSynth: Boosting Ejection Fraction Estimation Models via Controlled Video Diffusion](https://mlsanity.com/paper/2508.17631/) (2025)
- [Generative Regression for Left Ventricular Ejection Fraction Estimation from Echocardiography Video](https://mlsanity.com/paper/2602.08202/) (2026)
- [Echo-E3Net: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation](https://mlsanity.com/paper/2503.17543/) (2025)
- [Mamba-3D as Masked Autoencoders for Accurate and Data-Efficient Analysis of Medical Ultrasound Videos](https://mlsanity.com/paper/2503.20258/) (2025)
- [Heart Failure Prediction using Modal Decomposition and Masked Autoencoders for Scarce Echocardiography Databases](https://mlsanity.com/paper/2504.07606/) (2025)
- [Think as Cardiac Sonographers: Marrying SAM with Left Ventricular Indicators Measurements According to Clinical Guidelines](https://mlsanity.com/paper/2508.08566/) (2025)
- [A Unified DINOv2-Based Framework for LVEF Estimation, GLS Dysfunction Classification, and Early Cardiotoxicity Prediction](https://mlsanity.com/paper/2608.14750/) (2026)
- [The segmentation ceiling: why explicit left-ventricular masks do not improve learned ejection-fraction regression](https://mlsanity.com/paper/2609.19730/) (2026)

## Abstract (arXiv)

Learning spatiotemporal features is an important task for efficient video understanding especially in medical images such as echocardiograms. Convolutional neural networks (CNNs) and more recent vision transformers (ViTs) are the most commonly used methods with limitations per each. CNNs are good at capturing local context but fail to learn global information across video frames. On the other hand, vision transformers can incorporate global details and long sequences but are computationally expensive and typically require more data to train. In this paper, we propose a method that addresses the limitations we typically face when training on medical video data such as echocardiographic scans. The algorithm we propose (EchoCoTr) utilizes the strength of vision transformers and CNNs to tackle the problem of estimating the left ventricular ejection fraction (LVEF) on ultrasound videos. We demonstrate how the proposed method outperforms state-of-the-art work to-date on the EchoNet-Dynamic dataset with MAE of 3.95 and R2 of 0.82. These results show noticeable improvement compared to all published research. In addition, we show extensive ablations and comparisons with several algorithms, including ViT and BERT. The code is available at https://github.com/BioMedIA-MBZUAI/EchoCoTr.