Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks

• URL: http://arxiv.org/abs/2208.14635v1
• Date: Wed, 31 Aug 2022 05:06:00 GMT
• Title: Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks
• Authors: Shuo Chen and Da Ma and Sieun Lee and Timothy T.L. Yu and Gavin Xu and Donghuan Lu and Karteek Popuri and Myeong Jin Ju and Marinko V. Sarunic and Mirza Faisal Beg
• Abstract summary: This paper proposes a segmentation-guided domain-adaptation method to adapt images from multiple devices into single image domain. It avoids the time consumption of manual labelling for the upcoming new dataset and the re-training of the existing network.
• Score: 2.968191199408213
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Optical Coherence Tomography(OCT) is a non-invasive technique capturing cross-sectional area of the retina in micro-meter resolutions. It has been widely used as a auxiliary imaging reference to detect eye-related pathology and predict longitudinal progression of the disease characteristics. Retina layer segmentation is one of the crucial feature extraction techniques, where the variations of retinal layer thicknesses and the retinal layer deformation due to the presence of the fluid are highly correlated with multiple epidemic eye diseases like Diabetic Retinopathy(DR) and Age-related Macular Degeneration (AMD). However, these images are acquired from different devices, which have different intensity distribution, or in other words, belong to different imaging domains. This paper proposes a segmentation-guided domain-adaptation method to adapt images from multiple devices into single image domain, where the state-of-art pre-trained segmentation model is available. It avoids the time consumption of manual labelling for the upcoming new dataset and the re-training of the existing network. The semantic consistency and global feature consistency of the network will minimize the hallucination effect that many researchers reported regarding Cycle-Consistent Generative Adversarial Networks(CycleGAN) architecture.

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