Accurate segmentation of cardiac chambers in echocardiography sequences is crucial for the quantitative analysis of cardiac function, aiding in clinical diagnosis and treatment. The imaging noise, artifacts, and the deformation and motion of the heart pose challenges to segmentation algorithms.
While existing methods based on convolutional neural networks, Transformers and space-time memory networks, have improved segmentation accuracy, they often struggle with the trade-off between capturing long-range spatiotemporal dependencies and maintaining computational efficiency with fine-grained feature representation.
In this paper, we introduce GDKVM, a novel architecture for echocardiography video segmentation. The model employs Linear Key-Value Association (LKVA) to effectively model inter-frame correlations, and introduces Gated Delta Rule (GDR) to efficiently store intermediate memory states. Key-Pixel Feature Fusion (KPFF) module is designed to integrate local and global features at multiple scales, enhancing robustness against boundary blurring and noise interference.
We validated GDKVM on two mainstream echocardiography video datasets (CAMUS and EchoNet-Dynamic) and compared it with various state-of-the-art methods. Experimental results show that GDKVM outperforms existing approaches in terms of segmentation accuracy and robustness, while ensuring real-time performance.
超声心动图序列中心脏腔室的精确分割对于心脏功能的定量分析至关重要，有助于临床诊断和治疗。成像噪声、伪影以及心脏的变形和运动给分割算法带来了挑战。
虽然现有的基于卷积神经网络、Transformer和时空记忆网络的方法提高了分割精度，但它们往往难以在捕捉长程时空依赖关系与保持细粒度特征表示的计算效率之间取得平衡。
在本文中，我们介绍了 GDKVM，一种用于超声心动图视频分割的新颖架构。该模型采用线性键值关联（LKVA）有效地建模帧间相关性，并引入门控Delta规则（GDR）以高效存储中间记忆状态。关键像素特征融合（KPFF）模块旨在整合多尺度的局部和全局特征，增强对边界模糊和噪声干扰的鲁棒性。
我们在两个主流超声心动图视频数据集（CAMUS和EchoNet-Dynamic）上验证了GDKVM，并将其与各种最先进的方法进行了比较。实验结果表明，GDKVM在分割精度和鲁棒性方面优于现有方法，同时确保了实时性能。
