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. Code: this https URL.
Abstract (translated)
脑肿瘤的精确分割，尤其是通过注射对比剂后在磁共振成像（MRI）中可见的增强区域，对于临床诊断和治疗计划的准确性至关重要，但仍然具有挑战性。然而，当前的方法在分割这些增强型脑瘤区域时表现出显著性能下降，主要原因是未能充分考虑如复杂纹理和方向变化等特定于MRI的肿瘤特征。为了解决这个问题，我们提出了一种谐波频率融合网络（HFF-Net），该网络从频域角度重新思考了脑瘤分割的问题。 为了全面表征肿瘤区域，我们开发了一个频域分解（FDD）模块，将MRI图像分离成低频部分和高频部分。低频部分捕捉平滑的肿瘤轮廓，而高频部分则强调细节纹理和方向边缘。为进一步提高对肿瘤边界的敏感度，我们引入了自适应拉普拉斯卷积（ALC）模块，该模块通过动态更新的卷积核来增强关键的高频细节。 为了有效融合跨多个尺度的肿瘤特征，我们设计了一个频域交叉注意力（FDCA）机制，它整合了语义、位置和切片特定的信息。此外，我们还通过可视化分析、理论推理以及实验研究验证并解释了频率域改进的方法。 在四个公共数据集上的广泛实验证明，HFF-Net实现了平均相对4.48%的Dice分数提升（范围从2.39%到7.72%），主要表现在三大子区域中；同时，在对比增强肿瘤区域的分割上，其平均相对改进达到了7.33%（范围从5.96%至8.64%）。此外，HFF-Net在保持计算效率和临床应用性的同时展现了出色的性能。 代码链接：[此 URL](https://this-url.com)
URL
https://arxiv.org/abs/2506.10142
