Echocardiography Segmentation Using Neural ODE-based Diffeomorphic Registration Field

TL;DR

This paper introduces Echo-ODE, a Neural ODE-based diffeomorphic registration method for cardiac ultrasound video segmentation, achieving smoother, topologically consistent, and clinically accurate results over entire cardiac cycles.

Contribution

The paper presents a novel Neural ODE approach for diffeomorphic registration in echocardiography, improving temporal consistency and topology preservation in full-sequence segmentation.

Findings

Achieves smoother segmentation with Hausdorff distance 3.7-4.4

Maintains temporal consistency in 91% of videos

Preserves clinical accuracy with LVEF MAE of 2.7-3.1

Abstract

Convolutional neural networks (CNNs) have recently proven their excellent ability to segment 2D cardiac ultrasound images. However, the majority of attempts to perform full-sequence segmentation of cardiac ultrasound videos either rely on models trained only on keyframe images or fail to maintain the topology over time. To address these issues, in this work, we consider segmentation of ultrasound video as a registration estimation problem and present a novel method for diffeomorphic image registration using neural ordinary differential equations (Neural ODE). In particular, we consider the registration field vector field between frames as a continuous trajectory ODE. The estimated registration field is then applied to the segmentation mask of the first frame to obtain a segment for the whole cardiac cycle. The proposed method, Echo-ODE, introduces several key improvements compared to the previous state-of-the-art. Firstly, by solving a continuous ODE, the proposed method achieves smoother segmentation, preserving the topology of segmentation maps over the whole sequence (Hausdorff distance: 3.7-4.4). Secondly, it maintains temporal consistency between frames without explicitly optimizing for temporal consistency attributes, achieving temporal consistency in 91% of the videos in the dataset. Lastly, the proposed method is able to maintain the clinical accuracy of the segmentation maps (MAE of the LVEF: 2.7-3.1). The results show that our method surpasses the previous state-of-the-art in multiple aspects, demonstrating the importance of spatial-temporal data processing for the implementation of Neural ODEs in medical imaging applications. These findings open up new research directions for solving echocardiography segmentation tasks.