mlsanityData to 7 Oct 2026

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

arXiv 2207.02549 ↗First posted 6 Jul 2022cs.CVcited by 15

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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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 13 of its 25 references that are in the map. First posted on arXiv 6 Jul 2022.

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13 of 25 references.

References · 13

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  10. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  11. 2021Ultrasound Video Transformers for Cardiac Ejection Fraction Estimationcited by 34
  12. 2020Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentationcited by 7
1 more
  1. 2019Deep Learning Segmentation in 2D echocardiography using the CAMUS dataset : Automatic Assessment of the Anatomical Shape Validitycited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 3; 2025: 5; 2026: 2.

15 papers.

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  1. 2023GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosiscited by 9
  2. 2024CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysiscited by 8
  3. 2023Towards Robust Cardiac Segmentation using Graph Convolutional Networkscited by 3
  4. 2023Echocardiography Segmentation Using Neural ODE-based Diffeomorphic Registration Fieldcited by 2
  5. 2024EchoNarrator: Generating natural text explanations for ejection fraction predictionscited by 2
  6. 2023SimLVSeg: Simplifying Left Ventricular Segmentation in 2D+Time Echocardiograms with Self- and Weakly-Supervised Learningcited by 1
  7. 2024Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approachcited by 1
  8. 2025ControlEchoSynth: Boosting Ejection Fraction Estimation Models via Controlled Video Diffusioncited by 1
  9. 2025Hierarchical Spatio-temporal Segmentation Network for Ejection Fraction Estimation in Echocardiography Videoscited by 1
  10. 2026Generative Regression for Left Ventricular Ejection Fraction Estimation from Echocardiography Videocited by 1
  11. 2023ConFormer: A Novel Collection of Deep Learning Models to Assist Cardiologists in the Assessment of Cardiac Functioncited by 0
  12. 2025MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentationcited by 0
3 more
  1. 2025Echo-E3Net: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimationcited by 0
  2. 2025Mamba-3D as Masked Autoencoders for Accurate and Data-Efficient Analysis of Medical Ultrasound Videoscited by 0
  3. 2026A Quantitative Evaluation Framework for Temporal Explainability in Echocardiographic Video Segmentationcited by 0