Echo-ODE: A dynamics modeling network with neural ODE for temporally consistent segmentation of video echocardiograms

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Echocardiogram segmentation is vital for clinical diagnosis.
• Temporal consistency in video echocardiogram analysis presents a significant challenge.
• Stable segmentation of cardiac structures is crucial for automated interpretation.

Purpose of the Study:

• To introduce Echo-ODE, a novel framework for echocardiogram analysis.
• To model cardiac dynamics using neural ordinary differential equations for improved segmentation.
• To enhance spatio-temporal relationship learning in echocardiogram videos.

Main Methods:

• The proposed Echo-ODE framework treats the heart as a dynamical system.
• It utilizes neural ordinary differential equations to model cardiac dynamics.
• The model learns spatio-temporal relationships for continuous and consistent predictions.

Main Results:

• Echo-ODE demonstrates comparable accuracy to mainstream CNN models in segmentation.
• It achieves significantly improved temporal stability and consistency.
• The model shows superior performance in phase detection and robustness to arrhythmia.

Conclusions:

• Echo-ODE effectively addresses the need for temporal coherence in clinical video analysis.
• The framework offers a versatile backbone for various segmentation tasks.
• It holds great potential for reliable, fully automated video echocardiogram interpretation.