Noise-Aware Boundary-Enhanced Generative Learning for Ultrasound Speckle Reduction

2026-06-23

AI Summary

This work addresses the challenge of speckle noise in ultrasound imaging, which degrades image quality and obscures anatomical boundaries. Existing denoising methods often suffer from over-smoothing and limited generalizability. To overcome these limitations, the authors propose a Noise-aware Boundary-preserving Generative Learning (NBGL) framework that jointly models noise level estimation and boundary preservation for the first time. NBGL employs a dual-branch architecture—comprising speckle suppression and boundary enhancement pathways—and introduces a noise-aware weighted Feature-wise Linear Modulation (wFiLM) mechanism to enable adaptive feature fusion. Evaluated on 141 3D transvaginal ultrasound volumes across six noise levels, the method consistently outperforms state-of-the-art approaches, achieving superior denoising performance while significantly enhancing anatomical fidelity.

Abstract

Ultrasound is a non-invasive, real-time, and cost-effective imaging technique widely used in clinical diagnosis. However, its diagnostic efficacy is often compromised by inherent speckle noise that degrades image quality and obscures underlying anatomical structures. Existing speckle reduction methods tend to over-smooth tissue boundaries and generalize poorly to heterogeneous noise levels. To address these limitations, we propose a Noise-Aware Boundary-Enhanced Generative Learning (NBGL) framework for ultrasound speckle reduction, which simultaneously preserves annotated anatomical boundaries and adapts to varying noise levels. The NBGL framework consists of a speckle reduction branch and a boundary enhancement branch. The former leverages generative learning to suppress speckle noise, while the latter learns boundary-sensitive representations to preserve target anatomical structures. Furthermore, a noise-aware interaction weight generation (NIWG) module estimates the speckle noise level via 3D Laplacian filtering and a median absolute deviation estimator, and translates it into an adaptive interaction weight. This weight is incorporated into a weighted feature-wise linear modulation (wFiLM) module to adaptively modulate cross-branch feature coupling, thereby improving robustness to varying noise levels. Extensive evaluations on 141 3D transvaginal ultrasound volumes demonstrate that NBGL consistently outperforms state-of-the-art methods in speckle reduction and structural preservation across six noise levels, while maintaining consistency with annotated anatomical boundaries.

Problem

• speckle noise
• ultrasound imaging
• boundary preservation
• noise heterogeneity
• image quality degradation

Innovation

• Noise-aware
• Boundary enhancement
• Generative learning
• Speckle reduction
• Adaptive feature modulation