Domain Adaptation via CycleGAN for Retina Segmentation in Optical Coherence Tomography

TL;DR

This paper explores using CycleGAN for domain adaptation in retinal OCT images to improve AI model generalizability across different datasets without compromising medical data security.

Contribution

It introduces a CycleGAN-based method for adapting OCT images from the UK Biobank dataset to target domains, enhancing model transferability in medical imaging.

Findings

CycleGAN effectively adapts OCT images across domains.

Improved retinal layer segmentation performance after domain adaptation.

Provides a pipeline for domain adaptation using publicly available data.

Abstract

With the FDA approval of Artificial Intelligence (AI) for point-of-care clinical diagnoses, model generalizability is of the utmost importance as clinical decision-making must be domain-agnostic. A method of tackling the problem is to increase the dataset to include images from a multitude of domains; while this technique is ideal, the security requirements of medical data is a major limitation. Additionally, researchers with developed tools benefit from the addition of open-sourced data, but are limited by the difference in domains. Herewith, we investigated the implementation of a Cycle-Consistent Generative Adversarial Networks (CycleGAN) for the domain adaptation of Optical Coherence Tomography (OCT) volumes. This study was done in collaboration with the Biomedical Optics Research Group and Functional & Anatomical Imaging & Shape Analysis Lab at Simon Fraser University. In this study, we investigated a learning-based approach of adapting the domain of a publicly available dataset, UK Biobank dataset (UKB). To evaluate the performance of domain adaptation, we utilized pre-existing retinal layer segmentation tools developed on a different set of RETOUCH OCT data. This study provides insight on state-of-the-art tools for domain adaptation compared to traditional processing techniques as well as a pipeline for adapting publicly available retinal data to the domains previously used by our collaborators.