Generating Synthesized Ultrasound Biomicroscopy Images from Anterior Segment Optical Coherent Tomography Images by Generative Adversarial Networks for Iridociliary Assessment.

Introduction: The aim of this study was to investigate the feasibility of generating synthesized ultrasound biomicroscopy (UBM) images from swept-source anterior segment optical coherent tomography (SS-ASOCT) images using a cycle-consistent generative adversarial network framework (CycleGAN) for iridociliary assessment on a cohort presenting for primary angle-closure screening.

Methods: The CycleGAN architecture was adopted to synthesize high-resolution UBM images trained on the SS-ASOCT dataset from the department of ophthalmology, Xinhua Hospital. The performance of the CycleGAN model was further tested in two separate datasets using synthetic UBM images from two different ASOCT modalities (in-distribution and out-of-distribution).