Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT.
This paper proposes a new network framework, which leverages EfficientNetB4, attention gate, and residual learning techniques to achieve automatic and accurate liver segmentation. First, we use EfficientNetB4 as the encoder to extract more feature information during the encoding stage. Then, an atte...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 6; pp. 1479 - 1494 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160503246&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160503246 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2022 vid: 35 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160503246 157476493 160503246 160503246 10.1007/s10278-022-00668-x 160503246 ppf: 1479 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT. aug: au: Wang, Jinke Zhang, Xiangyang Lv, Peiqing Wang, Haiying Cheng, Yuanzhi affil: Department of Software Engineering, Harbin University of Science and Technology, No. 2006, Xueyuan Road, Shandong Province, 264300, Rongcheng City, China sug: subj: Liver Radiography Image Processing, Computer Assisted Methods Tomography, X-Ray Computed Methods Neural Networks (Computer) Deep Learning Digital Imaging Functional Residual Capacity Human Qualitative Studies Quantitative Studies Algorithms Coding ab: This paper proposes a new network framework, which leverages EfficientNetB4, attention gate, and residual learning techniques to achieve automatic and accurate liver segmentation. First, we use EfficientNetB4 as the encoder to extract more feature information during the encoding stage. Then, an attention gate is introduced in the skip connection to eliminate irrelevant regions and highlight features of a specific segmentation task. Finally, to alleviate the problem of gradient vanishment, we replace the traditional convolution of the decoder with a residual block to improve the segmentation accuracy. We verified the proposed method on the LiTS17 and SLiver07 datasets and compared it with classical networks such as FCN, U-Net, attention U-Net, and attention Res-U-Net. In the Sliver07 evaluation, the proposed method achieved the best segmentation performance on all five standard metrics. Meanwhile, in the LiTS17 assessment, the best performance is obtained except for a slight inferior on RVD. The proposed method's qualitative and quantitative results demonstrated its applicability in liver segmentation and proved its good prospect in computer-assisted liver segmentation. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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