Application of Opening and Closing Morphology in Deep Learning-Based Brain Image Registration  

作　　者：Yue Yang Shiyu Liu Shunbo Hu Lintao Zhang Jitao Li Meng Li Fuchun Zhang 

机构地区：[1]School of Information Science and Engineering,Linyi University,Shandong Linyi 276000,China

出　　处：《Journal of Beijing Institute of Technology》2023年第5期609-618,共10页北京理工大学学报（英文版）

基　　金：supported by Shandong Provincial Natural Science Foundation(No.ZR2023MF062);the National Natural Science Foundation of China(No.61771230).

摘　　要：In order to improve the registration accuracy of brain magnetic resonance images(MRI),some deep learning registration methods use segmentation images for training model.How-ever,the segmentation values are constant for each label,which leads to the gradient variation con-centrating on the boundary.Thus,the dense deformation field(DDF)is gathered on the boundary and there even appears folding phenomenon.In order to fully leverage the label information,the morphological opening and closing information maps are introduced to enlarge the non-zero gradi-ent regions and improve the accuracy of DDF estimation.The opening information maps supervise the registration model to focus on smaller,narrow brain regions.The closing information maps supervise the registration model to pay more attention to the complex boundary region.Then,opening and closing morphology networks(OC_Net)are designed to automatically generate open-ing and closing information maps to realize the end-to-end training process.Finally,a new registra-tion architecture,VM_(seg+oc),is proposed by combining OC_Net and VoxelMorph.Experimental results show that the registration accuracy of VM_(seg+oc) is significantly improved on LPBA40 and OASIS1 datasets.Especially,VM_(seg+oc) can well improve registration accuracy in smaller brain regions and narrow regions.

关 键 词：three dimensional(3D)medical image registration deep learning opening operation closing operation MORPHOLOGY 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术] R445.2[医药卫生—影像医学与核医学]