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Automatic quantification of superficial foveal avascular zone in optical coherence tomography angiography implemented with deep learning

Authors
  • Guo, Menglin1
  • Zhao, Mei2
  • Cheong, Allen M. Y.2
  • Dai, Houjiao1
  • Lam, Andrew K. C.2
  • Zhou, Yongjin1
  • 1 Shenzhen University, Shenzhen University Xili Campus, Room 208, Block A2,, Taoyuan Street, Shenzhen, 518055, China , Shenzhen (China)
  • 2 The Hong Kong Polytechnic University, Kowloon, Hong Kong, China , Hong Kong (China)
Type
Published Article
Journal
Visual Computing for Industry, Biomedicine, and Art
Publisher
Springer Singapore
Publication Date
Dec 09, 2019
Volume
2
Issue
1
Identifiers
DOI: 10.1186/s42492-019-0031-8
Source
Springer Nature
Keywords
License
Green

Abstract

An accurate segmentation and quantification of the superficial foveal avascular zone (sFAZ) is important to facilitate the diagnosis and treatment of many retinal diseases, such as diabetic retinopathy and retinal vein occlusion. We proposed a method based on deep learning for the automatic segmentation and quantification of the sFAZ in optical coherence tomography angiography (OCTA) images with robustness to brightness and contrast (B/C) variations. A dataset of 405 OCTA images from 45 participants was acquired with Zeiss Cirrus HD-OCT 5000 and the ground truth (GT) was manually segmented subsequently. A deep learning network with an encoder–decoder architecture was created to classify each pixel into an sFAZ or non-sFAZ class. Subsequently, we applied largest-connected-region extraction and hole-filling to fine-tune the automatic segmentation results. A maximum mean dice similarity coefficient (DSC) of 0.976 ± 0.011 was obtained when the automatic segmentation results were compared against the GT. The correlation coefficient between the area calculated from the automatic segmentation results and that calculated from the GT was 0.997. In all nine parameter groups with various brightness/contrast, all the DSCs of the proposed method were higher than 0.96. The proposed method achieved better performance in the sFAZ segmentation and quantification compared to two previously reported methods. In conclusion, we proposed and successfully verified an automatic sFAZ segmentation and quantification method based on deep learning with robustness to B/C variations. For clinical applications, this is an important progress in creating an automated segmentation and quantification applicable to clinical analysis.

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