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.
Highlights
Optical coherence tomography (OCT) has significantly advanced ophthalmic imaging, and OCT angiography (OCTA) is a noninvasive approach that provides a highresolution visualization of the vasculature in the retina and choroid without the injection of an intravenous contrast [1]
The maximum mean dice similarity coefficient (DSC) was obtained in a wide range of thresholds, and the mean DSC variation over the entire threshold range was gradual, which indicates that the performance was insensitive to the selected threshold
In the current study, we propose a segmentation and quantification method based on deep learning for automatically segmenting and quantifying the superficial FAZ (sFAZ) in the OCTA images with the best mean DSC of 0.976
Summary
Optical coherence tomography (OCT) has significantly advanced ophthalmic imaging, and OCT angiography (OCTA) is a noninvasive approach that provides a highresolution visualization of the vasculature in the retina and choroid without the injection of an intravenous contrast [1]. The areas of the sFAZ of both patients with diabetic retinopathy and patients with retinal vein occlusion are larger than those of healthy people [7, 8]. The sFAZ is negatively correlated with the best-corrected visual acuity [9]. Guo et al Visual Computing for Industry, Biomedicine, and Art (2019) 2:21 the sFAZ is positively correlated with the logarithm of the minimum angle of resolution of visual acuity [10]. Accurate segmentation and quantification of sFAZ is crucial for the diagnosis and treatment of the abovementioned diseases
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