HFCAS OpenIR
Multimodal Glioma Image Segmentation Using Dual Encoder Structure and Channel Spatial Attention Block
Su, Run1,2; Liu, Jinhuai1,2; Zhang, Deyun3; Cheng, Chuandong4,5,6; Ye, Mingquan7
2020-10-28
Source PublicationFRONTIERS IN NEUROSCIENCE
Corresponding AuthorLiu, Jinhuai(jhliu@iim.ac.cn)
AbstractMultimodal 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.
Keywordmedical image fusion glioma segmentation fully convolutional neural networks DES CSAB F-S-Net
DOI10.3389/fnins.2020.586197
WOS KeywordDEEP
Indexed BySCI
Language英语
Funding ProjectScience 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]
Funding OrganizationScience and Technology Project grant from Anhui Province ; Fundamental Research Fund for the Central Universities
WOS Research AreaNeurosciences & Neurology
WOS SubjectNeurosciences
WOS IDWOS:000588015400001
PublisherFRONTIERS MEDIA SA
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.hfcas.ac.cn:8080/handle/334002/105071
Collection中国科学院合肥物质科学研究院
Corresponding AuthorLiu, Jinhuai
Affiliation1.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.Univ Sci & Technol China, Div Life Sci & Med, Hefei, Peoples R China
6.Anhui Prov Key Lab Brain Funct & Brain Dis, Hefei, Peoples R China
7.Wannan Med Coll, Sch Med Informat, Wuhu, Peoples R China
Recommended Citation
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).
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