解决超声心动图分割的帧间抖动问题，提升时序稳定性。
A DyL-Unet framework based on dynamic learning for Temporally Consistent Echocardiographic Segmentation
- 基于动态学习构建心肌动力图，捕捉视频时序特征。
- 引入相位感知注意力机制，显著减少分割抖动。
- 适合临床自动化超声分析，提升诊断可靠性。
超声心动图中心脏结构的精准分割对心血管诊疗至关重要。然而，由于图像形变和斑点噪声，常导致帧间分割结果不稳定，即使单帧精度高，时序不一致仍会削弱功能评估并影响临床可读性。为此，我们提出DyL-UNet，一种基于动态学习的时序一致性分割框架。该框架通过动态学习构建心肌动力图（EDG），提取视频时序动态信息；采用多个Swin-Transformer编码器-解码器分支处理单帧图像；并在跳跃连接处引入心脏相位动态注意力（CPDA），利用EDG编码的动态特征与心脏相位线索，强化分割过程中的时序一致性。在CAMUS与EchoNet-Dynamic数据集上的大量实验表明，DyL-UNet在保持与现有方法相当的分割精度的同时，实现了更优的时序一致性，为自动化临床超声心动图分析提供了可靠解决方案。
原文摘要 · Abstract (English)
Accurate segmentation of cardiac anatomy in echocardiography is essential for cardiovascular diagnosis and treatment. Yet echocardiography is prone to deformation and speckle noise, causing frame-to-frame segmentation jitter. Even with high accuracy in single-frame segmentation, temporal instability can weaken functional estimates and impair clinical interpretability. To address these issues, we propose DyL-UNet, a dynamic learning-based temporal consistency U-Net segmentation architecture designed to achieve temporally stable and precise echocardiographic segmentation. The framework constructs an Echo-Dynamics Graph (EDG) through dynamic learning to extract dynamic information from videos. DyL-UNet incorporates multiple Swin-Transformer-based encoder-decoder branches for processing single-frame images. It further introduces Cardiac Phase-Dynamics Attention (CPDA) at the skip connections, which uses EDG-encoded dynamic features and cardiac-phase cues to enforce temporal consistency during segmentation. Extensive experiments on the CAMUS and EchoNet-Dynamic datasets demonstrate that DyL-UNet maintains segmentation accuracy comparable to existing methods while achieving superior temporal consistency, providing a reliable solution for automated clinical echocardiography.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。