FSUS-Seg: A Fusion-Spectral-Driven Universal Medical Ultrasound Image Segmentation Network

Abstract

Medical ultrasound images are often degraded by severe noise, low contrast and ambiguous anatomical boundaries. These characteristics limit segmentation methods that rely solely on spatial features, as they struggle to capture global context, local detail and precise boundary information. Spectral-domain modeling complements spatial features by capturing frequency-distribution patterns and multi-scale structural details. However, accurate segmentation of complex ultrasound images requires coordinated modeling of global frequency context, localized structural cues and long-range dependencies across network stages. To this end, we propose FSUS-Seg, a fusion-spectral driven universal network for medical ultrasound image segmentation with stage-specific spatial-spectral modeling. In the encoder, we design a Fast Spatial-Spectral Convolution (FSSC) block to extract and fuse local and global features through joint convolutional and Fourier-domain modeling, enabling coupled representations in the spatial and spectral domains. At the skip connections, we introduce a Wavelet-Mamba Cross-Scale Fusion (WMCSF) module that integrates multi-resolution global semantics with fine-grained details in the spectral domain. By exploiting Mamba’s capacity for efficient long-range dependency modeling, WMCSF helps bridge the semantic gap between the encoder and decoder. At the bottleneck, a Wavelet-Mamba module further strengthens the joint modeling of global semantics and boundary precision through discrete wavelet transform-based decomposition and reconstruction. Extensive experiments on four public ultrasound datasets and one external dataset show that FSUS-Seg consistently outperforms state-of-the-art methods. Its superior performance on an in-house photoacoustic multi-organ segmentation dataset further demonstrates strong cross-scenario generalization ability.