Glioma Segmentation-Oriented Multi-Modal MR Image Fusion With Adversarial Learning  

作　　者：Yu Liu Yu Shi Fuhao Mu Juan Cheng Xun Chen 

机构地区：[1]Department of Biomedical Engineering,Hefei University of Technology,Hefei 230009 [2]Anhui Province Key Laboratory of Measuring Theory and Precision Instrument,Hefei University of Technology,Hefei 230009,China [3]Department of Neurosurgery,the First Affiliated Hospital of USTC,Division of Life Sciences and Medicine,and also with the Department of Electronic Engineering and Information Science,University of Science and Technology of China,Hefei 230001,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第8期1528-1531,共4页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(62176081,61922075,62171176);the Fundamental Research Funds for the Central Universities(JZ2020HGPA0111,JZ2021HGPA0061);the USTC Research Funds of the Double First-Class Initiative(KY2100000123)。

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.

关 键 词：IMAGE IMAGE devoted 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] R739.41[自动化与计算机技术—计算机科学与技术]