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). We compared the ability of glaucoma specialists to assess the image quality of real and synthetic images. UBM measurements, including anterior chamber, iridociliary parameters, were compared between real and synthetic UBM images. Intra-class correlation coefficients, coefficients of variation, and Bland-Altman plots were used to assess the level of agreement. The Fréchet Inception Distance (FID) was measured to evaluate the quality of the synthetic images.

Results: The whole trained dataset included anterior chamber angle images, of which 4037 were obtained by SS-ASOCT and 2206 were obtained by UBM. The image quality of real versus synthetic SS-ASOCT images was similar as assessed by two glaucoma specialists. The Bland-Altman analysis also suggested high consistency between measurements of real and synthetic UBM images. In addition, there was fair to excellent agreement between real and synthetic UBM measurements for the in-distribution dataset (ICC range 0.48-0.97) and the out-of-distribution dataset (ICC range 0.52-0.86). The FID was 21.3 and 24.1 for the synthetic UBM images from the in-distribution and out-of-distribution datasets, respectively.

Conclusion: We developed a CycleGAN model to translate UBM images from non-contact SS-ASOCT images. The CycleGAN synthetic UBM images showed fair to excellent reproducibility when compared with real UBM images. Our results suggest that the CycleGAN technique is a promising tool to evaluate the iridociliary and anterior chamber in an alternative non-contact method.