Echo-E$^3$Net: Efficient Endo-Epi Spatio-Temporal Network for Ejection Fraction Estimation

• URL: http://arxiv.org/abs/2503.17543v1
• Date: Fri, 21 Mar 2025 21:24:44 GMT
• Title: Echo-E$^3$Net: Efficient Endo-Epi Spatio-Temporal Network for Ejection Fraction Estimation
• Authors: Moein Heidari, Afshin Bozorgpour, AmirHossein Zarif-Fakharnia, Dorit Merhof, Ilker Hacihaliloglu,
• Abstract summary: Left ventricular ejection fraction (LVEF) is a critical metric for assessing cardiac function, widely used in diagnosing heart failure and guiding clinical decisions.<n>Recent deep learning advancements have enhanced automation, yet many existing models are computationally demanding.<n>We propose Echo-E$3$Net, an efficient Endo-Epi-temporal network tailored for LVEF estimation.
• Score: 6.230277002017236
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Left ventricular ejection fraction (LVEF) is a critical metric for assessing cardiac function, widely used in diagnosing heart failure and guiding clinical decisions. Despite its importance, conventional LVEF estimation remains time-consuming and operator-dependent. Recent deep learning advancements have enhanced automation, yet many existing models are computationally demanding, hindering their feasibility for real-time clinical applications. Additionally, the interplay between spatial and temporal features is crucial for accurate estimation but is often overlooked. In this work, we propose Echo-E$^3$Net, an efficient Endo-Epi spatio-temporal network tailored for LVEF estimation. Our method introduces the Endo-Epi Cardial Border Detector (E$^2$CBD) module, which enhances feature extraction by leveraging spatial and temporal landmark cues. Complementing this, the Endo-Epi Feature Aggregator (E$^2$FA) distills statistical descriptors from backbone feature maps, refining the final EF prediction. These modules, along with a multi-component loss function tailored to align with the clinical definition of EF, collectively enhance spatial-temporal representation learning, ensuring robust and efficient EF estimation. We evaluate Echo-E$^3$Net on the EchoNet-Dynamic dataset, achieving a RMSE of 5.15 and an R$^2$ score of 0.82, setting a new benchmark in efficiency with 6.8 million parameters and only 8.49G Flops. Our model operates without pre-training, data augmentation, or ensemble methods, making it well-suited for real-time point-of-care ultrasound (PoCUS) applications. Our Code is publicly available on~\href{https://github.com/moeinheidari7829/Echo-E3Net}{\textcolor{magenta}{GitHub}}.

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