Lesion Segmentation in Gastroscopic Images Using Generative Adversarial Networks.
The segmentation of the lesion region in gastroscopic images is highly important for the detection and treatment of early gastric cancer. This paper proposes a novel approach for gastric lesion segmentation by using generative adversarial training. First, a segmentation network is designed to genera...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 3; pp. 459 - 469 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2022
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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=157184682&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157184682 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2022 vid: 35 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 157184682 155107390 157184682 157184682 10.1007/s10278-022-00591-1 157184682 ppf: 459 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Lesion Segmentation in Gastroscopic Images Using Generative Adversarial Networks. aug: au: Sun, Yaru Li, Yunqi Wang, Pengfei He, Dongzhi Wang, Zhiqiang affil: Faculty of Information Technology, Beijing University of Technology, Beijing, China sug: subj: Deep Learning Gastroscopy Stomach Neoplasms Diagnosis Early Detection of Cancer Methods Generative Adversarial Networks Human Diagnosis, Computer Assisted Sensitivity and Specificity ROC Curve Image Processing, Computer Assisted ab: The segmentation of the lesion region in gastroscopic images is highly important for the detection and treatment of early gastric cancer. This paper proposes a novel approach for gastric lesion segmentation by using generative adversarial training. First, a segmentation network is designed to generate accurate segmentation masks for gastric lesions. The proposed segmentation network adds residual blocks to the encoding and decoding path of U-Net. The cascaded dilated convolution is also added at the bottleneck of U-Net. The residual connection promotes information propagation, while dilated convolution integrates multi-scale context information. Meanwhile, a discriminator is used to distinguish the generated and real segmentation masks. The proposed discriminator is a Markov discriminator (Patch-GAN), which discriminates each N × N matrix in the image. In the process of network training, the adversary training mechanism is used to iteratively optimize the generator and the discriminator until they converge at the same time. The experimental results show that the dice, accuracy, and recall are 86.6%, 91.9%, and 87.3%, respectively. These metrics are significantly better than the existing models, which proves the effectiveness of this method and can meet the needs of clinical diagnosis and treatment. 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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