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

Lazaridis, G; Lorenzi, M; Ourselin, S; Garway-Heath, D; (2019) Enhancing OCT signal by fusion of GANs: Improving statistical power of glaucoma clinical trials. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. (pp. pp. 3-11). Springer: Cham, Switzerland. Green open access

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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.

Type: Proceedings paper
Title: Enhancing OCT signal by fusion of GANs: Improving statistical power of glaucoma clinical trials
Event: International Conference on Medical Image Computing and Computer-Assisted Intervention
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-32239-7_1
Publisher version: https://doi.org/10.1007/978-3-030-32239-7_1
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10089258
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