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

Introduction: Segmentation of echocardiograms plays a crucial role in clinical diagnosis. Beyond accuracy, a major challenge of video echocardiogram analysis is the temporal consistency of consecutive frames. Stable and consistent segmentation of cardiac structures is essential for a reliable fully automatic echocardiogram interpretation. Methods: We propose a novel framework Echo-ODE, where the heart is regarded as a dynamical system, and we model the representation of dynamics by neural ordinary differential equations. Echo-ODE learns the spatio-temporal relationships of the input video and output continuous and consistent predictions. Results: Experiments conducted on the Echo-Dynamic, the CAMUS and our private dataset demonstrate that Echo-ODE achieves comparable accuracy but significantly better temporal stability and consistency in video segmentation than previous mainstream CNN models. More accurate phase detection and robustness to arrhythmia also underscore the superiority of our proposed model. Discussion: Echo-ODE addresses the critical need for temporal coherence in clinical video analysis. This framework establishes a versatile backbone extendable beyond segmentation tasks. Its ability to model cardiac dynamics demonstrates great potential for enabling reliable, fully automated video echocardiogram interpretation. The code is publicly available at https://github.com/luwenlianglu/EchoODE.