Automatic echocardiography video segmentation is crucial for accurate diagnosis of cardiovascular diseases, as high-quality segmentation significantly improves automated lesion detection. However, deep learning methods still face challenges including speckle noise, dynamic ventricular changes, limited annotations, and the requirement for real-time inference in clinical practice. In this paper, we propose MSSNet, a novel semi-supervised method based on the efficient sequence modeling architecture Mamba, to address these challenges. To enhance noise robustness and foreground tracking, we design a flexible and efficient spatiotemporal synergistic guidance (SSG) module that leverages attention weights from historical frames to guide subsequent segmentation. By incorporating stable structural context and modeling inter-frame dependencies through weight propagation, SSG effectively mitigates segmentation errors caused by strong local noise and ventricular dynamics while maintaining low computational complexity. To alleviate limited annotation, we further introduce two semi-supervised modules: region-wise adaptive cross-mix (RAC) and dynamic offset correction (DOC). RAC simulates clinically plausible samples via regional mixing to enrich semantic details and strengthen feature learning, while DOC continuously integrates features from high-quality pseudo-labels during training. Experiments on the CAMUS and EchoNet-Dynamic datasets demonstrate that MSSNet outperforms existing SOTA methods in segmentation accuracy and achieves notable improvements in inference speed. The code is available at https://github.com/SSS666-klk/MSSNet.

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 https://github.com/wangrui2025/GDKVM.

Automatic echocardiography video segmentation is a powerful tool for improving the accuracy of cardiovascular function assessment. However, it remains a challenging task owing to (1) extensive speckle noise and blurred boundaries, (2) dramatic shape variations of targeting structures across frames, and (3) limited labeled data due to the high cost of annotation. In this paper, we present a novel semi-supervised segmentation model based on Vision Mamba (Vim) to comprehensively tackle these challenges; we call it EchoVim. Our framework introduces three technical innovations: First, a bidirectional inference mechanism (BIM) which can propagate label information bidirectionally from end-diastolic (ED) and end-systolic (ES) frames to generate pseudo-labels, coupled with confidence-aware dynamic updating to progressively refine supervision signals. Second, a dynamic interaction temporal alignment (DITA) module that establishes anatomical correspondence across frames by adaptively enhancing features near temporally stable regions while suppressing motion-irrelevant artifacts, effectively addressing variations in cardiac shape. Third, a semantic token-attentive refinement (STR) module that constructs low-rank semantic tokens to encode cardiac structure priors, utilizing attention-guided nonlinear transformations to disentangle speckle noise from true anatomical patterns. We conduct extensive experiments on two benchmarking echocardiography video datasets: CAMUS and EchoNet-Dynamic, and the results demonstrate that our method outperforms existing state-of-the-art approaches with real-time inference. Codes are available at https://github.com/guojx2255/EchoVim.

Automatic segmentation of echocardiography videos is crucial for computer-aided cardiovascular function assessment in clinical practice. However, it is a challenging task owing to the existence of massive speckle noise, the large shape variations of heart structures between frames, and limited annotations. In this paper, we propose a novel semi-supervised video segmentation model to comprehensively meet these challenges. The proposed approach has two key techniques. First, we propose a dual-stream architecture that processes spatial and temporal features through separate pathways to capture structural details and motion patterns, then enhances spatiotemporal representations by interacting these decomposed features with query features generated from the original input. Second, as speckle noise primarily concentrates in high-frequency regions, we extend the traditional dilated convolution from a frequency perspective, enabling it to adaptively adjust the dilation rate and convolution kernel weights based on high frequency speckle noise information. This enables the network to focus on specific frequency bands, thereby enhancing its ability to capture both low-frequency context and high-frequency local details. Extensive experiments on the CAMUS and EchoNet-Dynamic datasets demonstrate that our method outperforms existing state-of-the-art methods in terms of both accuracy and inference speed. Codes are available at https://github.com/guojx2255/HSCA-SDC.