心脏超声去雾的语义扩散后验采样

摘要

超声心动图在心脏成像中占据核心地位，其提供的动态心脏视图对诊断和监测至关重要。然而，图像质量常因多路径混响产生的雾霾而显著下降，尤其是在难以成像的患者中。本研究针对MICCAI超声心动图去雾挑战赛（DehazingEcho2025），提出了一种基于语义引导的扩散去雾算法。该方法将源自模糊输入语义分割的逐像素噪声模型，整合到一个由清洁超声数据训练的生成先验引导的扩散后验采样框架中。在挑战数据集上的定量评估显示，该算法在对比度和保真度指标上均表现出色。提交算法的代码已发布于https://github.com/tristan-deep/semantic-diffusion-echo-dehazing。

English

Echocardiography plays a central role in cardiac imaging, offering dynamic views of the heart that are essential for diagnosis and monitoring. However, image quality can be significantly degraded by haze arising from multipath reverberations, particularly in difficult-to-image patients. In this work, we propose a semantic-guided, diffusion-based dehazing algorithm developed for the MICCAI Dehazing Echocardiography Challenge (DehazingEcho2025). Our method integrates a pixel-wise noise model, derived from semantic segmentation of hazy inputs into a diffusion posterior sampling framework guided by a generative prior trained on clean ultrasound data. Quantitative evaluation on the challenge dataset demonstrates strong performance across contrast and fidelity metrics. Code for the submitted algorithm is available at https://github.com/tristan-deep/semantic-diffusion-echo-dehazing.

心脏超声去雾的语义扩散后验采样

Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing

摘要

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