EnGAN: Enhancement Generative Adversarial Network in Medical Image Segmentation Article Swipe
Erqiang Deng
,
Zhiguang Qin
,
Dajiang Chen
,
Zhen Qin
,
Yi Ding
,
Geng Ji
,
Ning Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1219874/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1219874/v1
Deep learning has been widely used in medical image segmentation, although the accuracy is affected by the problems of small sample space, data imbalance, and cross-device differences. Aiming at such issues, a enhancement GAN network is proposed by using the domain transferring of the adversarial generation network to enhance the original medical images. Specifically, based on retaining the transferability of the original GAN network, a new optimizer is added to generate a sample space with a continuous distribution, which can be used as the target domain of the original image transferring. The optimizer back-propagates the labels of the supervised data set through the segmentation network and maps the discrete distribution of the labels to the continuous image distribution, which has a high similarity to the original image but improves the segmentation efficiency.On this basis, the optimized distribution is taken as the target domain, and the generator and discriminator of the GAN network are trained so that the generator can transfer the original image distribution to the target distribution. extensive experiments are conducted based on MRI, CT, and ultrasound data sets. The experimental results show that, the proposed method has a good generalization effect in medical image segmentation, even when the data set has limited sample space and data imbalance to a certain extent.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1219874/v1
- https://www.researchsquare.com/article/rs-1219874/latest.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4205996016
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4205996016Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-1219874/v1Digital Object Identifier
- Title
-
EnGAN: Enhancement Generative Adversarial Network in Medical Image SegmentationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-11Full publication date if available
- Authors
-
Erqiang Deng, Zhiguang Qin, Dajiang Chen, Zhen Qin, Yi Ding, Geng Ji, Ning ZhangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-1219874/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-1219874/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-1219874/latest.pdfDirect OA link when available
- Concepts
-
Adversarial system, Generative grammar, Image (mathematics), Artificial intelligence, Segmentation, Generative adversarial network, Computer science, Image segmentation, Computer visionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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