摘要： 由于脑卒中的检查时间较长且治疗时间窗有限，因此研发一种快速且高准确性的脑卒中医学图像分割模型对于临床诊断具有重要意义。基于Mamba的U-Net架构具有较低复杂度以及大尺寸图像处理能力，近年来在医学图像处理领域得到广泛关注。分数阶傅里叶变换能够转换信号到空频域之间的任意分数域，在分数域内可以观测空频域中不显著的特征，故引入分数阶傅里叶变换，在分数域观察病灶特征。因此，基于分数阶傅里叶变换与Mamba网络，提出了一种针对脑卒中医学图像分割的模型FRFTMamba-UNet。该模型在Mamba网络中引入了分数域，并设计了一种与U-Net编码器相连接的多级残差模块，此外，在U-Net型网络中实现了分层特征提取策略，针对U-Net的浅层与深层分别设计了不同的特征提取模块，浅层添加了基于卷积神经网络的残差卷积以有效提取浅层特征，深层使用Mamba架构进一步提取深层特征。所提出方法的准确率和效率在AISD、ATLAS和ISLES22三个脑卒中数据集上普遍优于现有基于Mamba模块的SOTA模型，在AISD数据集上其Dice指标达到64.27%，ATLAS数据集上DSC指标达到62.24%，ISLES22数据集上其DSC指标达到85.24%。

Abstract: Due to the prolonged examination time and limited therapeutic time window for stroke, the development of a rapid and highly accurate medical image segmentation model for stroke is of significant importance for clinical diagnosis. The U-Net architecture based on Mamba, known for its low complexity and capability to handle large-scale images, has garnered widespread attention in the field of medical image processing in recent years. The fractional Fourier transform can convert signals into arbitrary fractional domains between the spatial and frequency domains, allowing the observation of features that are not prominent in the spatial or frequency domains. Therefore, by introducing the fractional Fourier transform, lesion characteristics can be observed in the fractional domain. Based on the fractional Fourier transform and the Mamba network, a novel model named FRFTMamba-UNet is proposed for stroke medical image segmentation. This model incorporates the fractional domain into the Mamba network and designs a multi-level residual module connected to the U-Net encoder. Additionally, a hierarchical feature extraction strategy is implemented in the U-Net-like network, where different feature extraction modules are designed for the shallow and deep layers. Specifically, residual convolutions based on convolutional neural networks are added to the shallow layers to effectively extract shallow features, while the Mamba architecture is utilized in the deep layers to further extract deep features. The proposed method demonstrates superior accuracy and efficiency compared to existing state-of-the-art models based on the Mamba module across three stroke datasets: AISD, ATLAS, and ISLES22. On the AISD dataset, it achieves a Dice score of 64.27%; on the ATLAS dataset, it achieves a DSC score of 62.24%; and on the ISLES22 dataset, it achieves a DSC score of 85.24%.