  Optical Coherence Tomography(OCT) is a non-invasive technique capturing
cross-sectional area of the retina in micro-meter resolutions. It has been
widely used as a auxiliary imaging reference to detect eye-related pathology
and predict longitudinal progression of the disease characteristics. Retina
layer segmentation is one of the crucial feature extraction techniques, where
the variations of retinal layer thicknesses and the retinal layer deformation
due to the presence of the fluid are highly correlated with multiple epidemic
eye diseases like Diabetic Retinopathy(DR) and Age-related Macular Degeneration
(AMD). However, these images are acquired from different devices, which have
different intensity distribution, or in other words, belong to different
imaging domains. This paper proposes a segmentation-guided domain-adaptation
method to adapt images from multiple devices into single image domain, where
the state-of-art pre-trained segmentation model is available. It avoids the
time consumption of manual labelling for the upcoming new dataset and the
re-training of the existing network. The semantic consistency and global
feature consistency of the network will minimize the hallucination effect that
many researchers reported regarding Cycle-Consistent Generative Adversarial
Networks(CycleGAN) architecture.
