OCT2Confocal: 3D CycleGAN based Translation of Retinal OCT Images to Confocal Microscopy
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| Format: | Preprint |
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2023
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| _version_ | 1866909368507695104 |
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| author | Tian, Xin Anantrasirichai, Nantheera Nicholson, Lindsay Achim, Alin |
| author_facet | Tian, Xin Anantrasirichai, Nantheera Nicholson, Lindsay Achim, Alin |
| contents | Optical coherence tomography (OCT) and confocal microscopy are pivotal in retinal imaging, each presenting unique benefits and limitations. In-vivo OCT offers rapid, non-invasive imaging but can be hampered by clarity issues and motion artifacts. Ex-vivo confocal microscopy provides high-resolution, cellular detailed color images but is invasive and poses ethical concerns and potential tissue damage. To bridge these modalities, we developed a 3D CycleGAN framework for unsupervised translation of in-vivo OCT to ex-vivo confocal microscopy images. Applied to our OCT2Confocal dataset, this framework effectively translates between 3D medical data domains, capturing vascular, textural, and cellular details with precision. This marks the first attempt to exploit the inherent 3D information of OCT and translate it into the rich, detailed color domain of confocal microscopy. Assessed through quantitative and qualitative evaluations, the 3D CycleGAN framework demonstrates commendable image fidelity and quality, outperforming existing methods despite the constraints of limited data. This non-invasive generation of retinal confocal images has the potential to further enhance diagnostic and monitoring capabilities in ophthalmology. Our source code and OCT2Confocal dataset are available at https://github.com/xintian-99/OCT2Confocal. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_10902 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | OCT2Confocal: 3D CycleGAN based Translation of Retinal OCT Images to Confocal Microscopy Tian, Xin Anantrasirichai, Nantheera Nicholson, Lindsay Achim, Alin Image and Video Processing Computer Vision and Pattern Recognition Optical coherence tomography (OCT) and confocal microscopy are pivotal in retinal imaging, each presenting unique benefits and limitations. In-vivo OCT offers rapid, non-invasive imaging but can be hampered by clarity issues and motion artifacts. Ex-vivo confocal microscopy provides high-resolution, cellular detailed color images but is invasive and poses ethical concerns and potential tissue damage. To bridge these modalities, we developed a 3D CycleGAN framework for unsupervised translation of in-vivo OCT to ex-vivo confocal microscopy images. Applied to our OCT2Confocal dataset, this framework effectively translates between 3D medical data domains, capturing vascular, textural, and cellular details with precision. This marks the first attempt to exploit the inherent 3D information of OCT and translate it into the rich, detailed color domain of confocal microscopy. Assessed through quantitative and qualitative evaluations, the 3D CycleGAN framework demonstrates commendable image fidelity and quality, outperforming existing methods despite the constraints of limited data. This non-invasive generation of retinal confocal images has the potential to further enhance diagnostic and monitoring capabilities in ophthalmology. Our source code and OCT2Confocal dataset are available at https://github.com/xintian-99/OCT2Confocal. |
| title | OCT2Confocal: 3D CycleGAN based Translation of Retinal OCT Images to Confocal Microscopy |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.10902 |