Speckle2Self: Self-Supervised Ultrasound Speckle Reduction Without Clean Data

2025-07-09

AI Summary

Ultrasound speckle noise exhibits tissue dependence and strong spatial correlation, rendering conventional self-supervised denoising methods—such as Noise2Noise and blind-spot networks—ineffective. To address this, we propose the first self-supervised ultrasound denoising framework requiring only a single noisy input image. Our method models multi-scale anatomical consistency and tissue-specific noise variations via multi-scale perturbations, and jointly separates tissue structures from noise by integrating low-rank–sparse decomposition priors with deep neural networks. Crucially, it operates without clean ground-truth labels, paired training data, or repeated acquisitions. Evaluated on both simulated and real carotid ultrasound datasets, our approach significantly outperforms state-of-the-art filtering and learning-based methods in quantitative metrics and visual quality. Moreover, it demonstrates strong generalization across different ultrasound devices, highlighting its clinical practicality and robustness.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Medical and Biological Imaging
• Search and Optimization: Non-convex Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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. extit{Code and datasets will be released upon acceptance.

Problem

• Reduces ultrasound speckle noise without clean data
• Handles tissue-dependent speckle using single noisy images
• Improves image quality across different ultrasound machines

Innovation

• Self-supervised speckle reduction without clean data
• Multi-scale perturbation for tissue-dependent variations
• Low-rank signal modeling for noise isolation