語義擴散後驗採樣用於心臟超聲去霧

摘要

心臟超音波在心血管成像中扮演著核心角色，提供動態的心臟視圖，對於診斷和監測至關重要。然而，圖像質量可能因多重路徑迴聲產生的霧霾而顯著下降，特別是在難以成像的患者中。在本研究中，我們提出了一種語義引導的、基於擴散的去霧算法，專為MICCAI去霧心臟超音波挑戰賽（DehazingEcho2025）開發。我們的方法將從模糊輸入的語義分割中導出的像素級噪聲模型，整合到由清潔超音波數據訓練的生成先驗引導的擴散後驗採樣框架中。在挑戰賽數據集上的定量評估顯示，該方法在對比度和保真度指標上均表現出色。提交算法的代碼可在https://github.com/tristan-deep/semantic-diffusion-echo-dehazing獲取。

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.

語義擴散後驗採樣用於心臟超聲去霧

Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing

摘要

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