Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks

TL;DR

This paper introduces a segmentation-guided domain adaptation method using CycleGAN to harmonize retinal OCT images from different devices, enabling effective segmentation without manual labeling or re-training.

Contribution

It proposes a novel segmentation-guided CycleGAN approach that reduces domain discrepancies in retinal OCT images, improving segmentation accuracy across multiple devices.

Findings

Effective domain adaptation of OCT images from different devices.

Reduced need for manual labeling and re-training.

Maintained semantic and global feature consistency.

Abstract

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.