Deep‐learning visualization enhancement method for optical coherence tomography angiography in dermatology Article Swipe
Jingjiang Xu
,
Xing Yuan
,
Yanping Huang
,
Jia Qin
,
Gongpu Lan
,
Haixia Qiu
,
Bo Yu
,
Haibo Jia
,
Haishu Tan
,
Shiyong Zhao
,
Zhongwu Feng
,
Lin An
,
Xunbin Wei
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/jbio.202200366
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/jbio.202200366
Optical coherence tomography angiography (OCTA) in dermatology usually suffers from low image quality due to the highly scattering property of the skin, the complexity of cutaneous vasculature, and limited acquisition time. Deep‐learning methods have achieved great success in many applications. However, the deep learning approach to improve dermatological OCTA images has not been investigated due to the requirement of high‐performance OCTA systems and difficulty of obtaining high‐quality images as ground truth. This study aims to generate proper datasets and develop a robust deep learning method to enhance the skin OCTA images. A swept‐source skin OCTA system was employed to create low‐quality and high‐quality OCTA images with different scanning protocols. We propose a model named vascular visualization enhancement generative adversarial network and adopt an optimized data augmentation strategy and perceptual content loss function to achieve better image enhancement effect with small amount of training data. We demonstrate the superiority of the proposed method in skin OCTA image enhancement by quantitative and qualitative comparisons.
Related Topics
Concepts
Deep learning
Computer science
Artificial intelligence
Optical coherence tomography
Visualization
Optical coherence tomography angiography
Image quality
Generative adversarial network
Computer vision
Coherence (philosophical gambling strategy)
Ground truth
Pattern recognition (psychology)
Image (mathematics)
Medicine
Radiology
Mathematics
Statistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/jbio.202200366
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/jbio.202200366
- OA Status
- bronze
- Cited By
- 6
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379768099
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4379768099Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/jbio.202200366Digital Object Identifier
- Title
-
Deep‐learning visualization enhancement method for optical coherence tomography angiography in dermatologyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-08Full publication date if available
- Authors
-
Jingjiang Xu, Xing Yuan, Yanping Huang, Jia Qin, Gongpu Lan, Haixia Qiu, Bo Yu, Haibo Jia, Haishu Tan, Shiyong Zhao, Zhongwu Feng, Lin An, Xunbin WeiList of authors in order
- Landing page
-
https://doi.org/10.1002/jbio.202200366Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/jbio.202200366Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/jbio.202200366Direct OA link when available
- Concepts
-
Deep learning, Computer science, Artificial intelligence, Optical coherence tomography, Visualization, Optical coherence tomography angiography, Image quality, Generative adversarial network, Computer vision, Coherence (philosophical gambling strategy), Ground truth, Pattern recognition (psychology), Image (mathematics), Medicine, Radiology, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 3, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
51Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.suffers | 9 |
| abstract_inverted_index.systems | 62 |
| abstract_inverted_index.usually | 8 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 41 |
| abstract_inverted_index.achieved | 35 |
| abstract_inverted_index.approach | 45 |
| abstract_inverted_index.datasets | 78 |
| abstract_inverted_index.employed | 98 |
| abstract_inverted_index.function | 132 |
| abstract_inverted_index.generate | 76 |
| abstract_inverted_index.learning | 44, 84 |
| abstract_inverted_index.property | 19 |
| abstract_inverted_index.proposed | 151 |
| abstract_inverted_index.scanning | 108 |
| abstract_inverted_index.strategy | 127 |
| abstract_inverted_index.training | 143 |
| abstract_inverted_index.vascular | 115 |
| abstract_inverted_index.coherence | 2 |
| abstract_inverted_index.cutaneous | 26 |
| abstract_inverted_index.different | 107 |
| abstract_inverted_index.obtaining | 66 |
| abstract_inverted_index.optimized | 124 |
| abstract_inverted_index.complexity | 24 |
| abstract_inverted_index.difficulty | 64 |
| abstract_inverted_index.generative | 118 |
| abstract_inverted_index.perceptual | 129 |
| abstract_inverted_index.protocols. | 109 |
| abstract_inverted_index.scattering | 18 |
| abstract_inverted_index.tomography | 3 |
| abstract_inverted_index.acquisition | 30 |
| abstract_inverted_index.adversarial | 119 |
| abstract_inverted_index.angiography | 4 |
| abstract_inverted_index.demonstrate | 146 |
| abstract_inverted_index.dermatology | 7 |
| abstract_inverted_index.enhancement | 117, 137, 157 |
| abstract_inverted_index.qualitative | 161 |
| abstract_inverted_index.requirement | 58 |
| abstract_inverted_index.superiority | 148 |
| abstract_inverted_index.augmentation | 126 |
| abstract_inverted_index.comparisons. | 162 |
| abstract_inverted_index.investigated | 54 |
| abstract_inverted_index.quantitative | 159 |
| abstract_inverted_index.vasculature, | 27 |
| abstract_inverted_index.applications. | 40 |
| abstract_inverted_index.low‐quality | 101 |
| abstract_inverted_index.visualization | 116 |
| abstract_inverted_index.dermatological | 48 |
| abstract_inverted_index.high‐quality | 67, 103 |
| abstract_inverted_index.swept‐source | 93 |
| abstract_inverted_index.Deep‐learning | 32 |
| abstract_inverted_index.high‐performance | 60 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5000356911, https://openalex.org/A5100730716 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I117331123, https://openalex.org/I20231570 |
| citation_normalized_percentile.value | 0.68110275 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |