Ihaff-Net: Adaptive Frequency-Domain Fusion for Intracerebral Hemorrhage Segmentation

Intracerebral hemorrhage (ICH) often leads to high disability and mortality rates, and accurate and rapid segmentation of hematoma regions is crucial for condition assessment and treatment planning. Existing neural network methods are unsatisfactory due to the irregularity of hematoma morphology and location, and limited generalization performance. To address these issues, this paper proposes an improved U-Net architecture, which incorporates two innovative modules: an Adaptive High-Frequency Domain Enhancement module and a Context-Aware Similarity Fusion module. The former leverages ICH's highsignal characteristics through frequency-domain enhancement and intensity-guided weighting to selectively enhance ICH regions, thereby improving the network's sensitivity to ICH. The latter enables efficient integration of multi-level contextual information by calculating feature similarity between decoder levels and adaptively filtering and fusing features according to spatial consistency, thereby improving segmentation performance. Experimental results demonstrate that our method achieves improved performance across multiple evaluation metrics compared to existing methods, and provides a more reliable tool for automated diagnosis of ICH.