期刊
BIOMEDICAL OPTICS EXPRESS
卷 11, 期 3, 页码 1580-1597出版社
Optica Publishing Group
DOI: 10.1364/BOE.387807
关键词
-
资金
- National Natural Science Foundation of China [61875123, 81421004]
- National Key Instrumentation Development Project of China [2013YQ030651]
- Natural Science Foundation of Hebei Province [H2019201378]
Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Meanwhile, deep learning has achieved rapid development in image-to-image translation tasks. Some studies have proposed applying deep learning models to OCTA reconstruction and have obtained preliminary results. However, current studies are mostly limited to a few specific deep neural networks. In this paper, we conducted a comparative study to investigate OCTA reconstruction using deep learning models. Four representative network architectures including single-path models, U-shaped models, generative adversarial network (GAN)-based models and multi-path models were investigated on a dataset of OCTA images acquired from rat brains. Three potential solutions were also investigated to study the feasibility of improving performance. The results showed that U-shaped models and multi-path models are two suitable architectures for OCTA reconstruction. Furthermore, merging phase information should be the potential improving direction in further research. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据