提升超声心动图分割精度，解决噪声干扰与结构模糊问题
Automatic Echocardiography Segmentation via Transition Probability Correlation for Stable Semantic Extraction
- 设计双组件模块，利用局部转移概率校正语义并增强纹理
- 在CAMUS和EchoNet-Dynamic数据集上分别达到93.87%和92.62%的Dice分数
- 适合需要高精度心腔分割的临床医生与医学影像研究者
尽管超声心动图对心血管诊断至关重要，但固有的散斑噪声和低信噪比常导致语义特征模糊、边界破碎，严重制约深度学习模型在复杂临床病例中的分割精度。心脏的时序运动对解剖结构识别至关重要。为此，我们提出STLSF模块，包含基于窗口匹配的语义校正组件与语义引导的纹理增强组件。通过利用局部转移概率相关性校正语义，并采用语义引导的纹理增强，有效缓解了因图像质量差导致的纹理不稳定性与语义歧义。此外，为帮助编码器适应超声成像特有的内在先验，提出频率感知去噪预训练方法。整体构建具有局部归纳偏置与长程依赖性的卷积网络。大量实验验证其性能达当前最优，在CAMUS数据集上获得93.87% Dice，HD95为3.29mm；在EchoNet-Dynamic上达92.62% Dice，HD95为2.73mm。
原文摘要 · Abstract (English)
While echocardiography is essential for cardiovascular diagnosis, inherent speckle noise and low signal-to-noise ratio often lead to ambiguous semantic features and fragmented boundaries. These limitations significantly hinder the segmentation accuracy of deep learning models in complex clinical cases. Moreover, temporal motion of the heart plays a critical role in recognizing anatomical structures. To address these challenges, we designed a STLSF module which comprises a window-matching-based semantic correction component and a semantics-guided texture enhancement component. By leveraging local transition probability correlations to correct semantics and employing semantics-guided texture enhancement, the STLSF module effectively mitigates texture instability and ambiguous semantic interpretations caused by disadvantaged echocardiography quality. Additionally, to facilitate the encoder's adaptation to the intrinsic priors of ultrasound-specific imaging patterns, we propose a frequency-aware denoising pre-training method. The entire work builds a convolution-based network with locality inductive bias and long-range dependencies. Extensive experiments confirm our SOTA performance, achieving 93.87\% Dice on CAMUS and 92.62\% on EchoNet-Dynamic, with respective HD95 values of 3.29mm and 2.73mm.
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