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

Menglin Guo, Mei Zhao, Allen MY Cheong, Federico Corvi, Xin Chen, Siping Chen, Yongjin Zhou, Andrew KC Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.
Original languageEnglish
Article number102456
JournalBiomedical Signal Processing and Control
Volume66
Early online date4 Feb 2021
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Automatic segmentation
  • Deep foveal avascular zone
  • Deep learning
  • Optical coherence tomography angiography

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics

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