PCaSFUA-Net: Spatial frequency collaboration and uncertainty-aware fusion for multimodal prostate cancer segmentation

Accurate prostate cancer segmentation is essential for disease progression assessment and prognostic evaluation. Multiparametric MRI (mpMRI) offers complementary structural and functional information, enabling more reliable lesion segmentation than single-modality MRI. However, existing methods still struggle to preserve high-frequency details and handle modality uncertainty, which limits multimodal fusion effectiveness and segmentation accuracy under ambiguous tumor boundaries, complex lesion morphology, and substantial inter-modality heterogeneity. To address these challenges, we propose PCaSFUA-Net, a multimodal prostate cancer segmentation network built upon a pretrained medical vision foundation model, enabling efficient task adaptation with minimal parameter updates. The proposed network integrates a unified multimodal enhancement and fusion (MEF) framework with a coarse-to-fine (C2F) strategy for spatial prompt refinement. Specifically, the MEF framework includes a spatial-frequency collaborative (SFC) module to enhance intra-modality boundary and texture representations and a modality uncertainty-aware fusion (MUAF) module to adaptively reweight multimodal features for robust cross-modal fusion. Extensive experiments on two public datasets and one private dataset show that PCaSFUA-Net consistently outperforms existing methods across multiple evaluation metrics.