Abstract
Accurate and temporally consistent segmentation of the left ventricle from echocardiography videos is essential for estimating the ejection fraction and assessing cardiac function. However, modeling spatiotemporal dynamics remains difficult due to severe speckle noise and rapid non-rigid deformations. Existing linear recurrent models offer efficient in-context associative recall for temporal tracking, but rely on unconstrained state updates, which cause progressive singular value decay in the state matrix, a phenomenon known as rank collapse, resulting in anatomical details being overwhelmed by noise. To address this, we propose OSA, a framework that constrains the state evolution on the Stiefel manifold. We introduce the Orthogonalized State Update (OSU) mechanism, which formulates the memory evolution as Euclidean projected gradient descent on the Stiefel manifold to prevent rank collapse and maintain stable temporal transitions. Furthermore, an Anatomical Prior-aware Feature Enhancement module explicitly separates anatomical structures from speckle noise through a physics-driven process, providing the temporal tracker with noise-resilient structural cues. Comprehensive experiments on the CAMUS and EchoNet-Dynamic datasets show that OSA achieves state-of-the-art segmentation accuracy and temporal stability, while maintaining real-time inference efficiency for clinical deployment. Codes are available at this https URL.
Abstract (translated)
准确且时序一致地从超声心动图视频中分割左心室，对于估算射血分数和评估心脏功能至关重要。然而，由于严重的斑点噪声和快速的非刚性形变，对时空动态的建模仍然困难。现有的线性循环模型虽能提供高效的上下文关联记忆以实现时序追踪，但其依赖无约束的状态更新，导致状态矩阵出现渐进奇异值衰减——即秩崩溃现象，使解剖细节被噪声淹没。针对此问题，我们提出了OSA框架，该框架将状态演化约束在Stiefel流形上。我们引入了正交状态更新（OSU）机制，将记忆演化表述为Stiefel流形上的欧几里得投影梯度下降，以防止秩崩溃并维持稳定的时序过渡。此外，解剖先验感知特征增强模块通过物理驱动过程明确分离解剖结构与斑点噪声，为时序追踪器提供抗噪声的结构化线索。在CAMUS和EchoNet-Dynamic数据集上的综合实验表明，OSA在实现最先进分割精度与时序稳定性的同时，保持了临床部署所需的实时推理效率。代码已公开于此https URL。
URL
https://arxiv.org/abs/2603.26188
