Jiang Z, Huang Z, Qiu B, Liu X, Meng X, You Y, Liu G, Zhou C, Yang K, Maier A, Ren Q, Lu Y (2020)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2020
Book Volume: 11
Pages Range: 1216-1632
Journal Issue: 3
DOI: 10.1364/BOE.387807
Open Access Link: https://www.osapublishing.org/boe/fulltext.cfm?uri=boe-11-3-1580
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.
APA:
Jiang, Z., Huang, Z., Qiu, B., Liu, X., Meng, X., You, Y.,... Lu, Y. (2020). Comparative Study of Deep Learning Models for Optical Coherence Tomography Angiography. Biomedical Optics Express, 11(3), 1216-1632. https://doi.org/10.1364/BOE.387807
MLA:
Jiang, Zhe, et al. "Comparative Study of Deep Learning Models for Optical Coherence Tomography Angiography." Biomedical Optics Express 11.3 (2020): 1216-1632.
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