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和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(FRFT)能够转换信号到空域和频域之间的任意分数域,在分数域内可以观测空域和频域中不显著的特征,故引入FRFT,在分数域观察病灶特征。因此,基于FRFT与Mamba网络,提出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(CNN)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。实验结果表明,所提出方法的准确率和效率在AISD、ATLAS和ISLES 2022这3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相似性系数(DSC)为64.27%,ATLAS数据集上DSC为62.24%,ISLES 2022数据集上DSC为85.24%。

关键词: 脑卒中病灶分割, Mamba, 分数阶傅里叶变换, 残差卷积神经网络, U-Net

Abstract: Because of 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 important for clinical diagnosis. The U-Net architecture based on Mamba, which is 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 order Fourier Transform (FRFT) can convert signals to 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 FRFT, the lesion characteristics can be observed in the fractional domain. Based on the FRFT and Mamba network, a novel model named FRFTMamba-UNet is proposed for stroke medical image segmentation. This model incorporates a fractional domain into the Mamba network and designs a multilevel residual module connected to a U-Net encoder. In addition, a hierarchical feature extraction strategy is implemented in a U-Net-like network, where different feature extraction modules are designed for shallow and deep layers. Specifically, residual convolutions based on Convolutional Neural Networks (CNN) are added to the shallow layers to extract shallow features effectively, whereas the Mamba architecture is utilized in the deep layers to extract deep features further. The proposed method demonstrates superior accuracy and efficiency to existing state-of-the-art models based on the Mamba module across three stroke datasets: AISD, ATLAS, and ISLES 2022. On the AISD dataset, it achieves a Dice Similarity Coefficient (DSC) of 64.27%, 62.24%, whereas on the ISLES 2022 dataset, it achieves a DSC of 85.24%.

Key words: stroke lesion segmentation, Mamba, FRactional Fourier Transform (FRFT), residual Convolutional Neural Network(CNN), U-Net

中图分类号: 

• TP391