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Multimodal Glioma Image Segmentation Using Dual Encoder Structure and Channel Spatial Attention Block

文献类型：期刊论文

作者Su, Run1,2; Liu, Jinhuai1,2; Zhang, Deyun3; Cheng, Chuandong4,5,6; Ye, Mingquan7
刊名FRONTIERS IN NEUROSCIENCE
出版日期2020-10-28
卷号14
关键词medical image fusion glioma segmentation fully convolutional neural networks DES CSAB F-S-Net
DOI10.3389/fnins.2020.586197
通讯作者Liu, Jinhuai(jhliu@iim.ac.cn)
英文摘要Multimodal medical images provide significant amounts of complementary semantic information. Therefore, multimodal medical imaging has been widely used in the segmentation of gliomas through computational neural networks. However, inputting images from different sources directly to the network does not achieve the best segmentation effect. This paper describes a convolutional neural network called F-S-Net that fuses the information from multimodal medical images and uses the semantic information contained within these images for glioma segmentation. The architecture of F-S-Net is formed by cascading two sub-networks. The first sub-network projects the multimodal medical images into the same semantic space, which ensures they have the same semantic metric. The second sub-network uses a dual encoder structure (DES) and a channel spatial attention block (CSAB) to extract more detailed information and focus on the lesion area. DES and CSAB are integrated into U-Net architectures. A multimodal glioma dataset collected by Yijishan Hospital of Wannan Medical College is used to train and evaluate the network. F-S-Net is found to achieve a dice coefficient of 0.9052 and Jaccard similarity of 0.8280, outperforming several previous segmentation methods.
WOS关键词DEEP
资助项目Science and Technology Project grant from Anhui Province[1508085QHl84] ; Science and Technology Project grant from Anhui Province[201904a07020098] ; Fundamental Research Fund for the Central Universities[WK 9110000032]
WOS研究方向Neurosciences & Neurology
语种英语
WOS记录号WOS:000588015400001
出版者FRONTIERS MEDIA SA
资助机构Science and Technology Project grant from Anhui Province ; Fundamental Research Fund for the Central Universities
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/105070]  
专题中国科学院合肥物质科学研究院
通讯作者Liu, Jinhuai
作者单位1.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei, Peoples R China
2.Univ Sci & Technol China, Sci Isl Branch, Grad Sch, Hefei, Peoples R China
3.Anhui Agr Univ, Sch Engn, Hefei, Peoples R China
4.Univ Sci & Technol China, Affiliated Hosp 1, Dept Neurosurg, Hefei, Peoples R China
5.Anhui Prov Key Lab Brain Funct & Brain Dis, Hefei, Peoples R China
6.Univ Sci & Technol China, Div Life Sci & Med, Hefei, Peoples R China
7.Wannan Med Coll, Sch Med Informat, Wuhu, Peoples R China
推荐引用方式
GB/T 7714		 Su, Run,Liu, Jinhuai,Zhang, Deyun,et al. Multimodal Glioma Image Segmentation Using Dual Encoder Structure and Channel Spatial Attention Block[J]. FRONTIERS IN NEUROSCIENCE,2020,14.
APA Su, Run,Liu, Jinhuai,Zhang, Deyun,Cheng, Chuandong,&Ye, Mingquan.(2020).Multimodal Glioma Image Segmentation Using Dual Encoder Structure and Channel Spatial Attention Block.FRONTIERS IN NEUROSCIENCE,14.
MLA Su, Run,et al."Multimodal Glioma Image Segmentation Using Dual Encoder Structure and Channel Spatial Attention Block".FRONTIERS IN NEUROSCIENCE 14(2020).

入库方式： OAI收割

来源：合肥物质科学研究院

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