结合频域先验和特征增强的心脏图像分割方法  

Cardiac Image Segmentation by Combining Frequency Domain Prior and Feature Enhancement

作　　者：陈柯炎 刘巧红 韩啸翔 林元杰 张维坤 Chen Keyan;Liu Qiaohong;Han Xiaoxiang;Lin Yuanjie;Zhang Weikun(School of Health Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;College of Medical Instruments,Shanghai University of Medicine and Health Sciences,Shanghai 201318,China)

机构地区：[1]上海理工大学健康科学与工程学院,上海200093 [2]上海健康医学院医疗器械学院,上海201318

出　　处：《激光与光电子学进展》2024年第10期359-368,共10页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61801288)。

摘　　要：针对心脏磁共振图像中的心脏子结构之间灰度差异小导致的边界不清、右心室区域形状大小多变等影响分割精度的问题,提出一种结合频域先验知识和特征融合增强的心脏磁共振图像分割网络。所提模型是一个由频域先验引导子网络和特征融合增强子网络组合而成的D形结构网络。首先,通过傅里叶变换将原始图像从空间域转换为频域,提取出高频的边缘特征,并将频域先验引导的子网络的低级特征与特征融合增强子网络的对应阶段进行特征拼接融合,以提高边缘识别的能力;其次,在特征融合增强子网络的跳转连接处引入具有局部和全局注意力机制的特征融合模块,提取上下文信息并获得丰富的纹理细节;最后,在网络底部引入Transformer模块,进一步提取长距离语义信息,增强模型表达能力,提高分割精度。在ACDC数据集上的实验结果表明,与现有方法相比,所提方法在客观指标和视觉效果上均取得最佳的效果,良好的心脏分割结果能为后续图像分析和临床诊断提供参考依据。A segmentation network of heart magnetic resonance image that combines prior knowledge in the frequency domain and feature fusion enhancement is proposed to address the issues of unclear boundaries caused due to the small grayscale differences among the heart substructures in heart magnetic resonance images and the varying shapes and sizes of the right ventricular region,affecting segmentation accuracy.The proposed model is a D-shaped structured network comprising a frequency domain prior guidance and feature fusion enhancer subnetworks.First,the original image is transformed from the spatial domain to the frequency domain using Fourier transform,extracting high-frequency edge features and combining the low-level features of the frequency domain prior-guided subnetwork with the corresponding stages of the feature fusion enhancement subnetwork for improving the edge recognition ability.Second,a feature fusion module with local and global attention mechanisms is introduced at the jump connection of the feature fusion enhancer network to extract contextual information and obtain rich texture details.Finally,the Transformer module is introduced at the bottom of the network to further extract long-distance semantic information,enhance the expression ability of the model,and improve segmentation accuracy.Experimental results on the ACDC dataset reveal that compared to existing methods,the proposed method achieves the best results in objective indicators and visual effects.Good cardiac segmentation results can provide reference for subsequent image analysis and clinical diagnosis.

关 键 词：图像分割 心脏磁共振图像 频域先验 注意力机制 傅里叶变换 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]