  Current state-of-the-art segmentation techniques for ocular images are
critically dependent on large-scale annotated datasets, which are
labor-intensive to gather and often raise privacy concerns. In this paper, we
present a novel framework, called BiOcularGAN, capable of generating synthetic
large-scale datasets of photorealistic (visible light and near-infrared) ocular
images, together with corresponding segmentation labels to address these
issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2
(DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic
Mask Generator (SMG) component that produces semantic annotations by exploiting
latent features of the DB-StyleGAN2 model. We evaluate BiOcularGAN through
extensive experiments across five diverse ocular datasets and analyze the
effects of bimodal data generation on image quality and the produced
annotations. Our experimental results show that BiOcularGAN is able to produce
high-quality matching bimodal images and annotations (with minimal manual
intervention) that can be used to train highly competitive (deep) segmentation
models (in a privacy aware-manner) that perform well across multiple real-world
datasets. The source code for the BiOcularGAN framework is publicly available
at https://github.com/dariant/BiOcularGAN.
