Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective

Shao, Minye; Wang, Zeyu; Duan, Haoran; Huang, Yawen; Zhai, Bing; Wang, Shizheng; Long, Yang; Zheng, Yefeng

Abstract

Precise segmentation of brain tumors, particularly contrast-enhancing regions visible in post-contrast MRI (areas highlighted by contrast agent injection), is crucial for accurate clinical diagnosis and treatment planning but remains challenging. However, current methods exhibit notable performance degradation in segmenting these enhancing brain tumor areas, largely due to insufficient consideration of MRI-specific tumor features such as complex textures and directional variations. To address this, we propose the Harmonized Frequency Fusion Network (HFF-Net), which rethinks brain tumor segmentation from a frequency-domain perspective. To comprehensively characterize tumor regions, we develop a Frequency Domain Decomposition (FDD) module that separates MRI images into low-frequency components, capturing smooth tumor contours and high-frequency components, highlighting detailed textures and directional edges. To further enhance sensitivity to tumor boundaries, we introduce an Adaptive Laplacian Convolution (ALC) module that adaptively emphasizes critical high-frequency details using dynamically updated convolution kernels. To effectively fuse tumor features across multiple scales, we design a Frequency Domain Cross-Attention (FDCA) integrating semantic, positional, and slice-specific information. We further validate and interpret frequency-domain improvements through visualization, theoretical reasoning, and experimental analyses. Extensive experiments on four public datasets demonstrate that HFF-Net achieves an average relative improvement of 4.48% (ranging from 2.39% to 7.72%) in the mean Dice scores across the three major subregions, and an average relative improvement of 7.33% (ranging from 5.96% to 8.64%) in the segmentation of contrast-enhancing tumor regions, while maintaining favorable computational efficiency and clinical applicability. Our code is available at: https://github.com/VinyehShaw/HFF.

Citation

Shao, M., Wang, Z., Duan, H., Huang, Y., Zhai, B., Wang, S., Long, Y., & Zheng, Y. (online). Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective. IEEE Transactions on Medical Imaging, https://doi.org/10.1109/tmi.2025.3579213

• Journal Article Type: Article
• Acceptance Date: Jun 6, 2025
• Online Publication Date: Jun 12, 2025
• Deposit Date: Jun 17, 2025
• Journal: IEEE Transactions on Medical Imaging
• Print ISSN: 0278-0062
• Electronic ISSN: 1558-254X
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/tmi.2025.3579213
• Public URL: https://durham-repository.worktribe.com/output/4106366