Can deep learning improve the automatic segmentation of deep foveal avascular zone in optical coherence tomography angiography?

Please use this identifier to cite or link to this item: 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
Appears in Collections:	Journal/Magazine Article

Status	embargoed access
Embargo End Date	2023-04-30

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