Abstract
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. Code is available at this https URL.
Abstract (translated)
在超声心动图序列中精确分割心脏腔室对于定量分析心脏功能至关重要，有助于临床诊断和治疗。然而，成像噪声、伪影以及心脏变形和运动等因素给分割算法带来了挑战。尽管基于卷积神经网络（CNN）、变压器和时空记忆网络的现有方法提高了分割精度，但它们在捕捉长程空间时间依赖性与保持计算效率和细粒度特征表示之间的权衡上仍然面临困难。 本文介绍了GDKVM，这是一种新颖的超声心动图视频分割架构。该模型采用了线性键值关联（LKVA）来有效建模帧间相关性，并引入了门控增量规则（GDR）以高效存储中间记忆状态。设计了关键像素特征融合（KPFF）模块，旨在在多个尺度上整合局部和全局特征，增强其对边界模糊和噪声干扰的鲁棒性。 我们在两个主流的超声心动图视频数据集(CAMUS 和 EchoNet-Dynamic)上验证了GDKVM，并将其与多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超过了现有方法，同时确保了实时性能。代码可在[此处](https://此链接应替换为实际的URL或描述，原文中未提供具体网址)获取。
URL
https://arxiv.org/abs/2512.10252
