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Cross-Device OCTA Generation by Patch-Based 3D Multi-Scale Feature Adaption
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AI
DOI：10.1109/TETCI.2023.3314690.png)
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Abstract
En 中文
Optical coherence tomography angiography (OCTA) is regarded as the best visualization of retinal vasculature and is widely used. However, the acquisition of OCTA is still limited by the device. In this study, we aim to generate OCTA from optical coherence tomography (OCT) images for devices without the OCTA imaging module. We proposed a novel unsupervised 3D domain adaption approach, termed Patch-based 3D Multi-scale Feature Adaption Networks (PMFAN), to transfer the OCTA generator trained on one device to other devices. To perform domain adaption, we first map the OCT of different devices to shared-feature spaces. To maintain fine-grained vessel features, we encode the OCT into a set of multi-scale feature codes. Besides, we model the domain adaption in 3D with a patch-based data manner. To reduce inaccurate feature coding due to incomplete context at the border of patches, we propose a novel context-enhanced encoder. Then, we generate OCTA from the feature codes by a domain-invariant OCTA generator. The experimental results on a dataset from two devices demonstrate that our method outperforms other unsupervised domain adaptation methods and image-translation-based methods in terms of the cross-device OCT to OCTA translation.
Keywords:
OCTA generation
domain adaption
optical coherence tomography
3D-CNN
patch-based manner
Journal
I
IF:
6.5
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1.4K
Citations:
4.5K