Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures.
Liver image segmentation has been increasingly employed for key medical purposes, including liver functional assessment, disease diagnosis, and treatment. In this work, we introduce a liver image segmentation method based on generative adversarial networks (GANs) and mask region-based convolutional...
| Publicado en: | BioMed Research International pp. 1 - 12 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
12/16/2021
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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=154175049&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154175049 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/16/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 154175049 154175049 154175049 10.1155/2021/9956983 154175049 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures. aug: au: Wei, Xiaoqin Chen, Xiaowen Lai, Ce Zhu, Yuanzhong Yang, Hanfeng Du, Yong affil: School of Medical Imaging, North Sichuan Medical College, Nanchong, Sichuan 637000, China sug: subj: Liver Radiography Tomography, X-Ray Computed Methods Imaging, Three-Dimensional Methods Neural Networks (Computer) Algorithms Human Correlation Coefficient Sensitivity and Specificity Liver Diseases Radiography Liver Diseases Classification Diagnosis, Computer Assisted Methods ab: Liver image segmentation has been increasingly employed for key medical purposes, including liver functional assessment, disease diagnosis, and treatment. In this work, we introduce a liver image segmentation method based on generative adversarial networks (GANs) and mask region-based convolutional neural networks (Mask R-CNN). Firstly, since most resulting images have noisy features, we further explored the combination of Mask R-CNN and GANs in order to enhance the pixel-wise classification. Secondly, k -means clustering was used to lock the image aspect ratio, in order to get more essential anchors which can help boost the segmentation performance. Finally, we proposed a GAN Mask R-CNN algorithm which achieved superior performance in comparison with the conventional Mask R-CNN, Mask-CNN, and k -means algorithms in terms of the Dice similarity coefficient (DSC) and the MICCAI metrics. The proposed algorithm also achieved superior performance in comparison with ten state-of-the-art algorithms in terms of six Boolean indicators. We hope that our work can be effectively used to optimize the segmentation and classification of liver anomalies. 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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