Abstract: [Objective] To address blurred lesion boundaries, high-frequency detail loss, and insufficient global modeling in medical image segmentation, we propose FreqUNet, a frequency–spatial dual-domain collaborative network for improving the segmentation accuracy and contour continuity of weak-boundary lesions. [Methods] A frequency–spatial dual-branch encoder was constructed based on U-Net, with a Multi-scale Frequency Filtering Module (MFFM) embedded in the first three encoder stages. The amplitude and phase spectra were decomposed using the two-dimensional Fast Fourier Transform. The amplitude spectrum was divided into low-frequency structural and high-frequency detail components, which were enhanced using a squeeze-and-excitation module and parallel 3×3, 5×5, and 7×7 multi-scale convolutions, respectively. A learnable parameter γ, initialized to 0.2, was introduced to adaptively regulate high-frequency energy. At the bottleneck layer, a Frequency-Space Dual Attention (FSDA) module was designed to fuse frequency-domain channel attention and spatial multi-scale atrous convolution features. A joint loss consisting of binary cross-entropy, Dice, and boundary losses was adopted with a weight ratio of 0.6:0.3:0.1. Comparative experiments were conducted under unified settings on the BUSI, ISIC, Kvasir, and CVC-ClinicDB datasets against U-Net, U-Net++, TransUNet, FAT-Net, Swin-UNet, H2Former, FFC, SFUNet, and PFESA. Ablation, generalization, and failure-case analyses were also performed. [Results] FreqUNet achieved an mDice of 0.8758 on BUSI, representing a 10.39 percentage point improvement over U-Net, with an HD95 of 13.34 pixels. The mDice values on ISIC, Kvasir, and CVC-ClinicDB were 0.8990, 0.8889, and 0.8886, respectively. FreqUNet achieved the best mDice and HD95 on BUSI, ISIC, and Kvasir, and generally outperformed the other comparison models except FAT-Net on CVC-ClinicDB. Bonferroni-corrected Wilcoxon signed-rank tests showed that FreqUNet significantly outperformed all comparison models on BUSI (p<0.0056), whereas its differences from FAT-Net on ISIC, Kvasir, and CVC-ClinicDB were not statistically significant. The model contained 35.84M parameters, involved 58.63 G floating-point operations, and achieved an inference speed of 144.81 FPS. Failure-case analysis showed that the model may still produce local false negatives, false positives, or boundary discontinuities in samples with extremely low contrast, strong noise, adhesive lesions, or tiny lesions. Three cross-dataset experiments demonstrated stable generalization performance on ISIC 2018→PH2, CVC-ClinicDB→ETIS-LaribPolypDB, and BUSI→UDIAT. Ablation experiments confirmed the synergy between adaptive amplitude enhancement and dual-domain attention: the complete model improved mDice by 9.66 percentage points over the baseline, and adding boundary loss reduced HD95 from 15.62 to 13.34 pixels. [Conclusion] FreqUNet improves structural representation and boundary delineation for weak-boundary lesions through frequency-domain decomposition, differentiated amplitude enhancement, and dual-domain adaptive fusion. It provides a useful approach for automatic segmentation of breast ultrasound, dermoscopic, and endoscopic images, although further optimization is needed for complex noise and extremely weak-boundary cases.

摘要： 【目的】 针对医学图像病灶分割中边界模糊、高频细节丢失及全局建模不足等问题，提出频域—空间双域协同分割网络FreqUNet，以提升弱边界病灶的分割精度和轮廓连续性。【方法】 以U-Net为基础构建频域与空间双分支编码结构，在编码器前3层嵌入多尺度频域增强模块（Multi-scale Frequency Filtering Module，MFFM）。通过二维快速傅里叶变换解耦幅值谱和相位谱，将幅值划分为低频结构分量和高频细节分量，分别采用压缩激励模块和3×3、5×5、7×7并行多尺度卷积进行增强，并利用初始值为0.2的可学习参数γ自适应调节高频能量。瓶颈层设计频域—空间双域注意力模块（Frequency-Space Dual Attention，FSDA），融合频域通道注意力和空间多尺度空洞卷积特征。训练采用二值交叉熵、Dice和边界损失构成的联合损失函数，权重比例为0.6∶0.3∶0.1。在BUSI、ISIC、Kvasir和CVC-ClinicDB四个公开数据集上，与U-Net、U-Net++、TransUNet、FAT-Net、Swin-UNet、H2Former、FFC、SFUNet和PFESA进行统一条件下的对比，并开展消融、泛化及失败案例分析。【结果】 FreqUNet在BUSI上的mDice为0.8758，较U-Net提高10.39个百分点，HD95降至13.34像素；在ISIC、Kvasir和CVC-ClinicDB上的mDice分别为0.8990、0.8889和0.8886。在BUSI、ISIC和Kvasir上，FreqUNet的mDice和HD95均取得最优；在CVC-ClinicDB上，除FAT-Net外整体优于其他对比模型。Wilcoxon符号秩检验经Bonferroni校正后显示，FreqUNet在BUSI上显著优于所有对比模型（p<0.0056），在ISIC、Kvasir和CVC-ClinicDB上与FAT-Net差异无统计学意义。模型参数量为35.84M，计算量为58.63G，推理速度为144.81 FPS。失败案例分析发现，模型在极低对比度、强噪声、病灶粘连和微小病灶等样本上仍可能出现局部漏检、误检或边界断裂。三组跨数据集实验表明，FreqUNet在ISIC 2018→PH2、CVC-ClinicDB→ETIS-LaribPolypDB和BUSI→UDIAT任务中具有稳定的泛化性能。消融实验表明，自适应幅值增强与双域注意力具有协同作用，完整模型较基线mDice提高9.66个百分点，加入边界损失后HD95由15.62像素降至13.34像素。【结论】 FreqUNet通过频域解耦、差异化幅值增强和双域自适应融合，有效提升了弱边界病灶的结构表达和边界刻画能力，可为乳腺超声、皮肤镜和内镜图像的自动分割提供参考，但在复杂噪声和极端弱边界场景下仍有进一步优化空间。