Open AccessComparative study of deep learning models for optical coherence tomography angiography
Author(s) -
Zhe Jiang,
Zhiyu Huang,
Bin Qiu,
Xiangxi Meng,
Yunfei You,
Xi Liu,
Gangjun Liu,
Chuangqing Zhou,
Kun Yang,
Andreas Maier,
Qiushi Ren,
Yanye Lu
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.387807
Subject(s) - deep learning , computer science , optical coherence tomography , artificial intelligence , generative adversarial network , coherence (philosophical gambling strategy) , optical coherence tomography angiography , artificial neural network , iterative reconstruction , machine learning , pattern recognition (psychology) , optics , physics , quantum mechanics
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.