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

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.

Automated summary

In this study, a customized deep learning network was used for accurately segmenting the foveal avascular zone (FAZ) in the deep retinal layer, achieving an average Dice coefficient of 0.88 and an average Hausdorff distance of 17.79 through six-fold cross-validation. The proposed method is expected to have good clinical application value and provide a more objective, faster, and spatially quantitative approach for investigations related to dFAZ.