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Abstract #91709 Published in IGR 21-4
Improving statistical power of glaucoma clinical trials using an ensemble of cyclical generative adversarial networks
Lazaridis G; Lazaridis G; Lorenzi M; Lorenzi M; Ourselin S; Ourselin S; Garway-Heath DMedical Image Analysis 2021; 68: 101906
Albeit spectral-domain OCT (SDOCT) is now in clinical use for glaucoma management, published clinical trials relied on time-domain OCT (TDOCT) which is characterized by low signal-to-noise ratio, leading to low statistical power. For this reason, such trials require large numbers of patients observed over long intervals and become more costly. We propose a probabilistic ensemble model and a cycle-consistent perceptual loss for improving the statistical power of trials utilizing TDOCT. TDOCT are converted to synthesized SDOCT and segmented via Bayesian fusion of an ensemble of GANs. The final retinal nerve fibre layer segmentation is obtained automatically on an averaged synthesized image using label fusion. We benchmark different networks using i) GAN, ii) Wasserstein GAN (WGAN) (iii) GAN + perceptual loss and iv) WGAN + perceptual loss. For training and validation, an independent dataset is used, while testing is performed on the UK Glaucoma Treatment Study (UKGTS), i.e. a TDOCT-based trial. We quantify the statistical power of the measurements obtained with our method, as compared with those derived from the original TDOCT. The results provide new insights into the UKGTS, showing a significantly better separation between treatment arms, while improving the statistical power of TDOCT on par with visual field measurements.
Centre for Medical Image Computing, University College London, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and the Institute of Ophthalmology, University College London, London, United Kingdom. Electronic address: g.lazaridis@cs.ucl.ac.uk.
Full articleClassification:
6.9.2.2 Posterior (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis > 6.9.2 Optical coherence tomography)
6.30 Other (Part of: 6 Clinical examination methods)
