  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.
