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http://hdl.handle.net/10397/89203
| Title: | Can deep learning improve the automatic segmentation of deep foveal avascular zone in optical coherence tomography angiography? | Authors: | Guo, M Zhao, M Cheong, AMY Corvi, F Chen, X Chen, S Zhou, Y Lam, AKC |
Issue Date: | Apr-2021 | Source: | Biomedical signal processing and control, Apr. 2021, v. 66, 102456 | Abstract: | Optical coherence tomography angiography (OCTA) is extensively used for visualizing retinal vasculature, including the foveal avascular zone (FAZ). Assessment of the FAZ is critical in the diagnosis and management of various retinal diseases. Accurately segmenting the FAZ in the deep retinal layer (dFAZ) is very challenging due to unclear capillary terminals. In this study, a customized encoder-decoder deep learning network was used for dFAZ segmentation. Six-fold cross-validation was performed on a total of 80 subjects (63 healthy subjects and 17 diabetic retinopathy subjects). The proposed method obtained an average Dice of 0.88 and an average Hausdorff distance of 17.79, suggesting the dFAZ was accurately segmented. The proposed method is expected to realize good clinical application value by providing an objective and faster and spatially-quantitative preparation of dFAZ-related investigations. | Keywords: | Deep learning Automatic segmentation Optical coherence tomography angiography Deep foveal avascular zone |
Publisher: | Elsevier | Journal: | Biomedical signal processing and control | ISSN: | 1746-8094 | DOI: | 10.1016/j.bspc.2021.102456 | Rights: | © 2021 Elsevier Ltd. All rights reserved. © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. The following publication Guo, M., Zhao, M., Cheong, A. M. Y., Corvi, F., Chen, X., Chen, S., Zhou, Y., & Lam, A. K. C. (2021). Can deep learning improve the automatic segmentation of deep foveal avascular zone in optical coherence tomography angiography? Biomedical Signal Processing and Control, 66, 102456 is available at https://dx.doi.org/10.1016/j.bspc.2021.102456. |
| Appears in Collections: | Journal/Magazine Article |
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| Guo_Deep_Learning_Automatic.pdf | Pre-Published version | 1.91 MB | Adobe PDF | View/Open |
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