Protecting Privacy while Improving Choroid Layer Segmentation in OCT Images: A GAN-based Image Synthesis Approach

The choroid, positioned behind the retina, nourishes the retina by supplying oxygen and nutrients. Choroidal structural changes are associated with severe vision-threatening conditions including age-related macular degeneration (AMD) and central serous chorioretinopathy (CSCR). Optical Coherence Tomography (OCT) imaging enables the visualization of choroidal changes, and clinicians rely on quantifying choroidal biomarkers through segmentation of the choroid layer in OCT scans for precise diagnosis and disease management. Accordingly, various attempts are made at automated choroid layer segmentation, however, their practicality is constrained by the limited and biased nature of training data. Privacy regulations hinder data aggregation, and supervised machine learning requires substantial annotated data. To tackle this, we propose an innovative image synthesis approach using generative adversarial networks (GANs). It involves a three-step process: generation of choroid-labeled B-scans using a standard GAN architecture, the transformation of these scans to unlabeled B-scans via Pix2Pix-GAN, and the training of a Pix2Pix-GAN choroid segmentation model using the synthesized data. To demonstrate the generalizability and efficacy, we evaluated the proposed choroid segmentation algorithm on the real B-scans from two different OCT imaging devices: enhanced depth imaging (EDI) and swept-source (SS) OCT, yielding Dice coefficient values of 84.84% and 85.15%, respectively, buttressing its effectiveness. Further, qualitative performance analysis, including manual grading, confirms that the synthesized choroid-labeled images are distinct from real images, thus ensuring data privacy. The proposed methodology marks an initial step towards developing a comprehensive choroid layer quantification tool using synthetic images, and its adaptability makes it versatile for various medical image segmentation challenges.