Novel Clinical Hybrid Deep Framework for Denoising and Anatomical Segmentation in Challenging Ultrasound Conditions

Abstract

Speckle noise in ultrasound imaging remains a major obstacle to accurate clinical interpretation and reliable anatomical segmentation. Existing enhancement methods often compromise anatomical details while reducing noise, particularly under challenging imaging conditions. To address this, we introduce an innovative hybrid framework combining the Smart Adaptive Framework for Image Enhancement (SAFIE), a denoising engine based on adaptive fractional convolutions and gradient-based refinement, with a segmentation strategy integrating superpixel-based hypergraph modeling and neural ordinary differential equations. This framework enables effective noise suppression and precise segmentation of anatomical structures by capturing both spatial coherence and temporal feature dynamics. The enhanced images reveal improved visibility of anatomical structures and boundaries. Qualitative evaluation by four experienced radiologists confirmed this improvement, with strong inter-observer agreement measured by Fleiss’ kappa, highlighting the robustness and clinical relevance of the approach. Quantitative results corroborate these observations, demonstrating performance substantially superior to several state-of-the-art methods. Ablation studies further indicate that each component contributes significantly to overall improvement. These findings suggest that the proposed framework enhances segmentation reliability and provides robust support for diagnostic interpretation in ultrasound imaging.