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.

翻译：卷积神经网络（CNN）近期在二维心脏超声图像分割中展现出卓越性能。然而，现有心脏超声视频全序列分割方法多依赖于仅在关键帧图像上训练的模型，或无法在时序过程中保持拓扑结构。为解决上述问题，本文提出基于神经常微分方程（Neural ODE）的微分同胚图像配准新方法，将超声视频分割视为配准估计问题。具体而言，我们将帧间配准场向量场视为连续轨迹常微分方程（ODE），进而将估计得到的配准场应用于首帧分割掩膜，从而获取完整心动周期的分割结果。所提方法Echo-ODE相较现有最优技术实现多项关键突破：其一，通过求解连续ODE获得更平滑的分割结果，使全序列分割图的拓扑结构得以保持（豪斯多夫距离：3.7-4.4）；其二，在不显式优化时序一致性属性的前提下，自动维持帧间时序一致性（数据集中91%视频达到时序一致）；其三，该方法能保持分割图的临床精度（左心室射血分数平均绝对误差：2.7-3.1）。结果表明，本方法在多个维度超越现有最优技术，验证了时空数据处理对医学影像中Neural ODE应用的重要价值。这些发现为超声心动图分割任务开辟了新的研究方向。