# Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound

arXiv: https://arxiv.org/abs/2207.02549 · HTML: https://mlsanity.com/paper/2207.02549/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation](https://mlsanity.com/paper/1705.08302/) (2017)
- [Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation](https://mlsanity.com/paper/1905.02876/) (2019)
- [SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator](https://mlsanity.com/paper/1911.05856/) (2019)
- [Motion Guided 3D Pose Estimation from Videos](https://mlsanity.com/paper/2004.13985/) (2020)
- [Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization](https://mlsanity.com/paper/1908.00748/) (2019)
- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation](https://mlsanity.com/paper/2107.00977/) (2021)
- [Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation](https://mlsanity.com/paper/2004.00074/) (2020)
- [Deep Learning Segmentation in 2D echocardiography using the CAMUS dataset : Automatic Assessment of the Anatomical Shape Validity](https://mlsanity.com/paper/1908.02994/) (2019)

## Built on by (most cited first, 15 of 15)

- [GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis](https://mlsanity.com/paper/2308.13217/) (2023)
- [CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis](https://mlsanity.com/paper/2403.10164/) (2024)
- [Towards Robust Cardiac Segmentation using Graph Convolutional Networks](https://mlsanity.com/paper/2310.01210/) (2023)
- [Echocardiography Segmentation Using Neural ODE-based Diffeomorphic Registration Field](https://mlsanity.com/paper/2306.09687/) (2023)
- [EchoNarrator: Generating natural text explanations for ejection fraction predictions](https://mlsanity.com/paper/2410.23744/) (2024)
- [SimLVSeg: Simplifying Left Ventricular Segmentation in 2D+Time Echocardiograms with Self- and Weakly-Supervised Learning](https://mlsanity.com/paper/2310.00454/) (2023)
- [Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach](https://mlsanity.com/paper/2402.19062/) (2024)
- [ControlEchoSynth: Boosting Ejection Fraction Estimation Models via Controlled Video Diffusion](https://mlsanity.com/paper/2508.17631/) (2025)
- [Hierarchical Spatio-temporal Segmentation Network for Ejection Fraction Estimation in Echocardiography Videos](https://mlsanity.com/paper/2508.18681/) (2025)
- [Generative Regression for Left Ventricular Ejection Fraction Estimation from Echocardiography Video](https://mlsanity.com/paper/2602.08202/) (2026)
- [ConFormer: A Novel Collection of Deep Learning Models to Assist Cardiologists in the Assessment of Cardiac Function](https://mlsanity.com/paper/2312.08567/) (2023)
- [MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentation](https://mlsanity.com/paper/2501.07120/) (2025)
- [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)
- [A Quantitative Evaluation Framework for Temporal Explainability in Echocardiographic Video Segmentation](https://mlsanity.com/paper/2609.08043/) (2026)

## Abstract (arXiv)

Accurate and consistent predictions of echocardiography parameters are important for cardiovascular diagnosis and treatment. In particular, segmentations of the left ventricle can be used to derive ventricular volume, ejection fraction (EF) and other relevant measurements. In this paper we propose a new automated method called EchoGraphs for predicting ejection fraction and segmenting the left ventricle by detecting anatomical keypoints. Models for direct coordinate regression based on Graph Convolutional Networks (GCNs) are used to detect the keypoints. GCNs can learn to represent the cardiac shape based on local appearance of each keypoint, as well as global spatial and temporal structures of all keypoints combined. We evaluate our EchoGraphs model on the EchoNet benchmark dataset. Compared to semantic segmentation, GCNs show accurate segmentation and improvements in robustness and inference runtime. EF is computed simultaneously to segmentations and our method also obtains state-of-the-art ejection fraction estimation. Source code is available online: https://github.com/guybenyosef/EchoGraphs.