论文标题
推动无监督学习的极限以进行超声图像删除
Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal
论文作者
论文摘要
超声（US）成像是一种快速且非侵入性的成像方式，可广泛用于实时临床成像应用，而无需与辐射危险有关。不幸的是，它通常遭受各种起源的视觉质量较差，例如斑点噪声，模糊，多线获取（MLA），有限的RF通道，用于平面波浪成像情况的少量视角等。处理这些问题的经典方法包括使用各种自适应过滤和基于模型的方法来处理这些问题。最近，深度学习方法已成功用于超声成像场。但是，这些方法的局限性之一是在许多实际应用中很难获得配对的高质量培训图像。在本文中，灵感来自最新的使用最佳运输驱动Cyclegan（OT-Cyclegan）的无监督学习理论，我们调查了无监督的深度学习在没有匹配的参考数据的情况下适用于US Artifact删除问题。各种任务的实验结果，例如反卷积，斑点去除，有限的数据伪影等。证实我们的无监督学习方法为许多实际应用提供了可比的结果。
Ultrasound (US) imaging is a fast and non-invasive imaging modality which is widely used for real-time clinical imaging applications without concerning about radiation hazard. Unfortunately, it often suffers from poor visual quality from various origins, such as speckle noises, blurring, multi-line acquisition (MLA), limited RF channels, small number of view angles for the case of plane wave imaging, etc. Classical methods to deal with these problems include image-domain signal processing approaches using various adaptive filtering and model-based approaches. Recently, deep learning approaches have been successfully used for ultrasound imaging field. However, one of the limitations of these approaches is that paired high quality images for supervised training are difficult to obtain in many practical applications. In this paper, inspired by the recent theory of unsupervised learning using optimal transport driven cycleGAN (OT-cycleGAN), we investigate applicability of unsupervised deep learning for US artifact removal problems without matched reference data. Experimental results for various tasks such as deconvolution, speckle removal, limited data artifact removal, etc. confirmed that our unsupervised learning method provides comparable results to supervised learning for many practical applications.
