用双噪声扩散模型精准分割超声心动图中的左心室
Echo-DND: A dual noise diffusion model for robust and precise left ventricle segmentation in echocardiography
- 设计双噪声扩散架构，融合高斯与伯努利噪声提升鲁棒性
- 在CAMUS和EchoNet-Dynamic上分别达0.962和0.939的Dice分数
- 适合医学图像分割研究者，尤其关注超声图像处理的团队
扩散概率模型在图像处理中取得突破，展现出在医疗领域的巨大潜力。准确分割超声心动图中的左心室对诊断和治疗至关重要。然而，超声图像普遍存在噪声大、对比度低、边界模糊等问题，显著增加分割难度。为此，本文提出面向该任务的新型双噪声扩散模型Echo-DND，创新性地结合高斯与伯努利噪声，引入多尺度融合条件模块以提升分割精度，并采用空间一致性校准机制保障分割掩码的空间完整性。在CAMUS和EchoNet-Dynamic数据集上进行严格验证，结果表明，该框架优于现有最先进模型，在两个数据集上的Dice分数分别达到0.962和0.939。Echo-DND为超声心动图分割树立了新标准，其架构亦有望拓展至其他医学影像任务，助力提升多领域诊断准确性。
原文摘要 · Abstract (English)
Recent advancements in diffusion probabilistic models (DPMs) have revolutionized image processing, demonstrating significant potential in medical applications. Accurate segmentation of the left ventricle (LV) in echocardiograms is crucial for diagnostic procedures and necessary treatments. However, ultrasound images are notoriously noisy with low contrast and ambiguous LV boundaries, thereby complicating the segmentation process. To address these challenges, this paper introduces Echo-DND, a novel dual-noise diffusion model specifically designed for this task. Echo-DND leverages a unique combination of Gaussian and Bernoulli noises. It also incorporates a multi-scale fusion conditioning module to improve segmentation precision. Furthermore, it utilizes spatial coherence calibration to maintain spatial integrity in segmentation masks. The model's performance was rigorously validated on the CAMUS and EchoNet-Dynamic datasets. Extensive evaluations demonstrate that the proposed framework outperforms existing SOTA models. It achieves high Dice scores of 0.962 and 0.939 on these datasets, respectively. The proposed Echo-DND model establishes a new standard in echocardiogram segmentation, and its architecture holds promise for broader applicability in other medical imaging tasks, potentially improving diagnostic accuracy across various medical domains. Project page: https://abdur75648.github.io/Echo-DND
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