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

ABSTRACT:

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 acc

SUBMITTER: Lu W 

PROVIDER: S-EPMC12399644 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature