A Transfer Deep Generative Adversarial Network Model to Synthetic Brain CT Generation from MR Images Article Swipe
Background . The generation of medical images is to convert the existing medical images into one or more required medical images to reduce the time required for sample diagnosis and the radiation to the human body from multiple medical images taken. Therefore, the research on the generation of medical images has important clinical significance. At present, there are many methods in this field. For example, in the image generation process based on the fuzzy C‐means (FCM) clustering method, due to the unique clustering idea of FCM, the images generated by this method are uncertain of the attribution of certain organizations. This will cause the details of the image to be unclear, and the resulting image quality is not high. With the development of the generative adversarial network (GAN) model, many improved methods based on the deep GAN model were born. Pix2Pix is a GAN model based on UNet. The core idea of this method is to use paired two types of medical images for deep neural network fitting, thereby generating high‐quality images. The disadvantage is that the requirements for data are very strict, and the two types of medical images must be paired one by one. DualGAN model is a network model based on transfer learning. The model cuts the 3D image into multiple 2D slices, simulates each slice, and merges the generated results. The disadvantage is that every time an image is generated, bar‐shaped “shadows” will be generated in the three‐dimensional image. Method/Material . To solve the above problems and ensure the quality of image generation, this paper proposes a Dual3D&PatchGAN model based on transfer learning. Since Dual3D&PatchGAN is set based on transfer learning, there is no need for one‐to‐one paired data sets, only two types of medical image data sets are needed, which has important practical significance for applications. This model can eliminate the bar‐shaped “shadows” produced by DualGAN’s generated images and can also perform two‐way conversion of the two types of images. Results . From the multiple evaluation indicators of the experimental results, it can be analyzed that Dual3D&PatchGAN is more suitable for the generation of medical images than other models, and its generation effect is better.
Related Topics
Concepts
Computer science
Artificial intelligence
Deep learning
Generative model
Field (mathematics)
Image (mathematics)
Medical imaging
Artificial neural network
Process (computing)
Cluster analysis
Pattern recognition (psychology)
Computer vision
Generative grammar
Machine learning
Pure mathematics
Operating system
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2021/9979606
- https://downloads.hindawi.com/journals/wcmc/2021/9979606.pdf
- OA Status
- hybrid
- Cited By
- 9
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3159125378
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3159125378Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2021/9979606Digital Object Identifier
- Title
-
A Transfer Deep Generative Adversarial Network Model to Synthetic Brain CT Generation from MR ImagesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Yi Gu, Qiankun ZhengList of authors in order
- Landing page
-
https://doi.org/10.1155/2021/9979606Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/wcmc/2021/9979606.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/wcmc/2021/9979606.pdfDirect OA link when available
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Computer science, Artificial intelligence, Deep learning, Generative model, Field (mathematics), Image (mathematics), Medical imaging, Artificial neural network, Process (computing), Cluster analysis, Pattern recognition (psychology), Computer vision, Generative grammar, Machine learning, Pure mathematics, Operating system, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2024: 5, 2023: 1, 2022: 1, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3094599226, https://openalex.org/W2921168875, https://openalex.org/W3081896987, https://openalex.org/W2977262154, https://openalex.org/W3021977528, https://openalex.org/W3085519426, https://openalex.org/W2965390715, https://openalex.org/W3003011734, https://openalex.org/W3013236627, https://openalex.org/W2998832642, https://openalex.org/W2917706746, https://openalex.org/W2778924750, https://openalex.org/W3088971987, https://openalex.org/W2982599183, https://openalex.org/W2907719205, https://openalex.org/W3034577681, https://openalex.org/W2963768110, https://openalex.org/W2967834561, https://openalex.org/W3048323270, https://openalex.org/W3047977341, https://openalex.org/W3023908013, https://openalex.org/W3009367116, https://openalex.org/W2915387487, https://openalex.org/W3135891860, https://openalex.org/W3082951775, https://openalex.org/W2021326137, https://openalex.org/W2964481084, https://openalex.org/W3039655629, https://openalex.org/W2896754632, https://openalex.org/W2053677366, https://openalex.org/W2995616826, https://openalex.org/W2984306354, https://openalex.org/W2608015370, https://openalex.org/W2951523806, https://openalex.org/W2775584822, https://openalex.org/W3033703941, https://openalex.org/W2963444790, https://openalex.org/W3102190437 |
| referenced_works_count | 38 |
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| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5014319412 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I111599522 |
| citation_normalized_percentile.value | 0.76085806 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |