典型文献
Glioma Segmentation-Oriented Multi-Modal MR Image Fusion With Adversarial Learning
文献摘要：
Dear Editor,
In recent years,multi-modal medical image fusion has received widespread attention in the image processing community.However,existing works on medical image fusion methods are mostly devoted to pursuing high performance on visual perception and objective fusion metrics,while ignoring the specific purpose in clinical applications.In this letter,we propose a glioma segmentation-oriented multi-modal magnetic resonance(MR)image fusion method using an adversarial learning framework,which adopts a segmentation network as the discriminator to achieve more meaningful fusion results from the perspective of the segmentation task.Experimental results demonstrate the advantage of the proposed method over some state-of-the-art medical image fusion methods.
文献关键词：
中图分类号：
作者姓名：
Yu Liu;Yu Shi;Fuhao Mu;Juan Cheng;Xun Chen
作者机构：
Department of Biomedical Engineering,Hefei University of Technology,Hefei 230009;Anhui Province Key Laboratory of Measuring Theory and Precision Instrument,Hefei University of Technology,Hefei 230009,China;Department of Neurosurgery,the First Affiliated Hospital of USTC,Division of Life Sciences and Medicine,University of Science and Technology of China,Hefei 230001,China;Department of Electronic Engineering and Information Science,University of Science and Technology of China,Hefei 230001,China
文献出处：
引用格式：
[1]Yu Liu;Yu Shi;Fuhao Mu;Juan Cheng;Xun Chen-.Glioma Segmentation-Oriented Multi-Modal MR Image Fusion With Adversarial Learning)[J].自动化学报（英文版）,2022(08):1528-1531
A类：
B类：
Glioma,Segmentation,Oriented,Multi,Modal,MR,Image,Fusion,With,Adversarial,Learning,Dear,Editor,
In,recent,years,multi,modal,medical,image,fusion,has,received,widespread,attention,processing,community,However,existing,works,methods,are,mostly,devoted,pursuing,high,performance,visual,perception,objective,metrics,while,ignoring,specific,purpose,clinical,applications,this,letter,glioma,segmentation,oriented,magnetic,resonance,using,adversarial,learning,framework,which,adopts,network,discriminator,achieve,more,meaningful,results,from,perspective,task,Experimental,demonstrate,advantage,proposed,over,some,state,art
AB值：
0.716806
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
