基于小波变换和残差网络的磁共振影像分割方法  

Magnetic resonance image segmentation method based on wavelet transformation and residual network

作　　者：杜新彦[1] DU Xinyan(Press Center,Hebei Normal University,Shijiazhuang 050024,China)

机构地区：[1]河北师范大学新闻中心,河北石家庄050024

出　　处：《光学技术》2021年第2期250-256,共7页Optical Technique

摘　　要：为了提高磁共振图像分割的准确度,提出一种基于残差网络和小波变换的磁共振图像分割方法。采用离散小波变换对核共振图像的不同序列进行融合,使融合图像包含更加丰富的纹理信息和结构信息;提出了包含通道注意力模块和空间注意力模块的残差网络模型,使网络重点关注于目标分割区域,并加入残差块来缓解深度神经网络的梯度消失问题。最终在公开的Brain Tumor Segmentation Challenge 2015数据集上完成了验证实验,结果显示该方法在对完整肿瘤区域、核心肿瘤区域及增强肿瘤区域的平均Dice相似性系数均取得了较好的效果。In order to improve the accuracy of magnetic resonance image segmentation,a magnetic resonance image segmentation method based on residual network and wavelet transformation is proposed.First of all,the discrete wavelet transformation is adopted to fuse different sequences of magnetic resonance images,it leads the fusion image contains more texture information and structure information;then,a residual network including channel attention model and spatial attention model is proposed,thus the network can focus on the target region,and the residual block is included to reduce the vanishing gradient problem of deep neural networks.Finally,validation experiments are carried on the public Brain Tumor Segmentation Challenge 2015 dataset,the results show that the proposed method achieves good effect of average Dice score for whole tumor area,core tumor region and enhanced tumor region.

关 键 词：医学图像 核共振图像 图像分割 脑肿瘤检测 残差网络 离散小波变换 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术] TH691.9[自动化与计算机技术—计算机科学与技术]