Speckle2Self: Self-supervised ultrasound speckle reduction without clean data

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xuesong Li , Nassir Navab , Zhongliang Jiang
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引用次数: 0

Abstract

Image denoising is a fundamental task in computer vision, particularly in medical ultrasound (US) imaging, where speckle noise significantly degrades image quality. Although recent advancements in deep neural networks have led to substantial improvements in denoising for natural images, these methods cannot be directly applied to US speckle noise, as it is not purely random. Instead, US speckle arises from complex wave interference within the body microstructure, making it tissue-dependent. This dependency means that obtaining two independent noisy observations of the same scene, as required by pioneering Noise2Noise, is not feasible. Additionally, blind-spot networks also cannot handle US speckle noise due to its high spatial dependency. To address this challenge, we introduce Speckle2Self, a novel self-supervised algorithm for speckle reduction using only single noisy observations. The key insight is that applying a multi-scale perturbation (MSP) operation introduces tissue-dependent variations in the speckle pattern across different scales, while preserving the shared anatomical structure. This enables effective speckle suppression by modeling the clean image as a low-rank signal and isolating the sparse noise component. To demonstrate its effectiveness, Speckle2Self is comprehensively compared with conventional filter-based denoising algorithms and SOTA learning-based methods, using both realistic simulated US images and human carotid US images. Additionally, data from multiple US machines are employed to evaluate model generalization and adaptability to images from unseen domains. Project page: https://noseefood.github.io/us-speckle2self/.
Speckle2Self:无需清洁数据的自我监督超声斑点减少
图像去噪是计算机视觉的一项基本任务,特别是在医学超声成像中,散斑噪声会显著降低图像质量。尽管深度神经网络的最新进展在自然图像的去噪方面取得了实质性的进步,但这些方法不能直接应用于美国散斑噪声,因为它不是纯粹随机的。相反,美国斑点是由人体微观结构内的复杂波干涉产生的,使其与组织有关。这种依赖性意味着获得同一场景的两个独立的噪声观测,如先驱Noise2Noise所要求的那样,是不可行的。此外,盲点网络由于其高度的空间依赖性,也无法处理美国散斑噪声。为了解决这一挑战,我们引入了Speckle2Self,这是一种仅使用单个噪声观测的新型自监督散斑减少算法。关键的观点是,应用多尺度扰动(MSP)操作在不同尺度上引入了斑点模式的组织依赖性变化,同时保留了共享的解剖结构。通过将干净图像建模为低秩信号并隔离稀疏噪声成分,可以有效地抑制散斑。为了证明其有效性,Speckle2Self与传统的基于滤波器的去噪算法和基于SOTA学习的方法进行了全面的比较,使用了真实的模拟US图像和人类颈动脉US图像。此外,使用来自多个美国机器的数据来评估模型的泛化和对未知领域图像的适应性。项目页面:https://noseefood.github.io/us-speckle2self/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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