DBENet: Dual-Branch Encoder Network for brain MRI lesion segmentation

Abstract

Introduction Accurate brain lesion segmentation in Magnetic Resonance Imaging (MRI) remains challenging due to heterogeneous lesion appearance, variable scales, and ambiguous boundaries. Methods We propose DBENet, a Dual-Branch Encoder Network for brain MRI lesion segmentation. The spatial and frequency branches first extract lesion-related features from complementary domains. The Spatial and Frequency Fusion (SFF) module then integrates these features into a hybrid lesion representation. In parallel, lesion-aware prompts are encoded by the Segment Anything Model (SAM) prompt encoder to generate prompt embeddings. Finally, the Multi-scale Attention Fusion (MAF) module takes the hybrid representation and prompt embeddings as inputs, and progressively integrates them across scales for contextual modeling and boundary refinement. Results Extensive experiments on the ISLES 2022 and BraTS 2018 datasets demonstrate that DBENet outperforms state-of-the-art approaches, achieving Dice scores of 0.8621 and 0.8261, respectively, while providing superior lesion localization and boundary delineation. Ablation studies confirm the complementary benefits of SFF and MAF. Discussion Furthermore, DBENet achieves a favorable balance between segmentation accuracy and computational cost, supporting practical implementation under moderate hardware constraints. Future work will extend DBENet to volumetric 3D segmentation. We will also investigate multimodal learning and foundation-model adaptation to improve robustness across diverse imaging protocols.