Enhancing OCT signal by fusion of GANs: Improving statistical power of glaucoma clinical trials

Abstract

Accurately monitoring the efficacy of disease-modifying drugs in glaucoma therapy is of critical importance. Albeit high resolution spectral-domain optical coherence tomography (SDOCT) is now in widespread clinical use, past landmark glaucoma clinical trials have used time-domain optical coherence tomography (TDOCT), which leads, however, to poor statistical power due to low signal-to-noise characteristics. Here, we propose a probabilistic ensemble model for improving the statistical power of imaging-based clinical trials. TDOCT are converted to synthesized SDOCT images and segmented via Bayesian fusion of an ensemble of generative adversarial networks (GANs). The proposed model integrates super resolution (SR) and multi-atlas segmentation (MAS) in a principled way. Experiments on the UK Glaucoma Treatment Study (UKGTS) show that the model successfully combines the strengths of both techniques (improved image quality of SR and effective label propagation of MAS), and produces a significantly better separation between treatment arms than conventional segmentation of TDOCT.