  Optical coherence tomography (OCT) and confocal microscopy are pivotal in
retinal imaging, offering distinct advantages and limitations. In vivo OCT
offers rapid, non-invasive imaging but can suffer from clarity issues and
motion artifacts, while ex vivo confocal microscopy, providing high-resolution,
cellular-detailed color images, is invasive and raises ethical concerns. To
bridge the benefits of both modalities, we propose a novel framework based on
unsupervised 3D CycleGAN for translating unpaired in vivo OCT to ex vivo
confocal microscopy images. 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. We also introduce a unique dataset,
OCT2Confocal, comprising mouse OCT and confocal retinal images, facilitating
the development of and establishing a benchmark for cross-modal image
translation research. Our model has been evaluated both quantitatively and
qualitatively, achieving Fr\'echet Inception Distance (FID) scores of 0.766 and
Kernel Inception Distance (KID) scores as low as 0.153, and leading subjective
Mean Opinion Scores (MOS). Our model demonstrated superior image fidelity and
quality with limited data over existing methods. Our approach effectively
synthesizes color information from 3D confocal images, closely approximating
target outcomes and suggesting enhanced potential for diagnostic and monitoring
applications in ophthalmology.
