  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.
