Domain Adaptation via CycleGAN for Retina Segmentation in Optical Coherence Tomography

• URL: http://arxiv.org/abs/2107.02345v1
• Date: Tue, 6 Jul 2021 02:07:53 GMT
• Title: Domain Adaptation via CycleGAN for Retina Segmentation in Optical Coherence Tomography
• Authors: Ricky Chen, Timothy T. Yu, Gavin Xu, Da Ma, Marinko V. Sarunic, Mirza Faisal Beg
• Abstract summary: We investigated the implementation of a Cycle-Consistent Generative Adrative Networks (CycleGAN) for the domain adaptation of Optical Coherence Tomography ( OCT) volumes. This study was done in collaboration with the Biomedical Optics Research Group and Functional & Anatomical Imaging & Shape Analysis Lab at Simon Fraser University.
• Score: 0.09490124006642771
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the FDA approval of Artificial Intelligence (AI) for point-of-care clinical diagnoses, model generalizability is of the utmost importance as clinical decision-making must be domain-agnostic. A method of tackling the problem is to increase the dataset to include images from a multitude of domains; while this technique is ideal, the security requirements of medical data is a major limitation. Additionally, researchers with developed tools benefit from the addition of open-sourced data, but are limited by the difference in domains. Herewith, we investigated the implementation of a Cycle-Consistent Generative Adversarial Networks (CycleGAN) for the domain adaptation of Optical Coherence Tomography (OCT) volumes. This study was done in collaboration with the Biomedical Optics Research Group and Functional & Anatomical Imaging & Shape Analysis Lab at Simon Fraser University. In this study, we investigated a learning-based approach of adapting the domain of a publicly available dataset, UK Biobank dataset (UKB). To evaluate the performance of domain adaptation, we utilized pre-existing retinal layer segmentation tools developed on a different set of RETOUCH OCT data. This study provides insight on state-of-the-art tools for domain adaptation compared to traditional processing techniques as well as a pipeline for adapting publicly available retinal data to the domains previously used by our collaborators.

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