Enhanced OCT chorio-retinal segmentation in low-data settings with semi-supervised GAN augmentation using cross-localisation Article Swipe
Jason Kugelman
,
David Alonso‐Caneiro
,
Scott A. Read
,
Stephen J. Vincent
,
Michael J. Collins
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.cviu.2023.103852
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.cviu.2023.103852
Training deep learning methods for optical coherence tomography (OCT) retinal and choroidal layer segmentation is a challenge when data is scarce. In medical image analysis, this is often the case with a lack of data sharing due to confidentiality agreements and data privacy concerns which is further exacerbated in cases of rare pathologies. Even where OCT data is readily available, performing the requisite annotations is time consuming, costly, and error-prone. Data augmentation and semi-supervised learning (SSL) are two techniques employed in deep learning to enhance training in these situations. In this study, we extend our previous work proposing an enhanced StyleGAN2-based data augmentation method for OCT images by employing SSL through a novel cross-localisation technique. The technique increases the diversity of the synthetic data by automatically incorporating styles from unlabeled data with those from labelled data. The method can be used to extend StyleGAN2 as the core idea is simple, yet highly performant. In this work, we optimize the method through a set of ablations and propose the use of a targeted task-specific model selection technique for more optimal generator selection, further boosting performance. The method is applied to OCT retinal and choroidal layer segmentation, demonstrating its effectiveness through substantial patch classification performance improvements as well as significant reductions in choroidal layer segmentation error.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.cviu.2023.103852
- OA Status
- hybrid
- Cited By
- 3
- References
- 30
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387335053Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.cviu.2023.103852Digital Object Identifier
- Title
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Enhanced OCT chorio-retinal segmentation in low-data settings with semi-supervised GAN augmentation using cross-localisationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-10-04Full publication date if available
- Authors
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Jason Kugelman, David Alonso‐Caneiro, Scott A. Read, Stephen J. Vincent, Michael J. CollinsList of authors in order
- Landing page
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https://doi.org/10.1016/j.cviu.2023.103852Publisher landing page
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.cviu.2023.103852Direct OA link when available
- Concepts
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Computer science, Segmentation, Artificial intelligence, Optical coherence tomography, Deep learning, Boosting (machine learning), Pattern recognition (psychology), Semi-supervised learning, Data set, Machine learning, Labeled data, Medicine, OphthalmologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | Computer Vision and Image Understanding |
| primary_location.landing_page_url | https://doi.org/10.1016/j.cviu.2023.103852 |
| publication_date | 2023-10-04 |
| publication_year | 2023 |
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