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.

中文翻译：

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通过深度学习实现光学相干断层扫描血管造影中浅表性黄凹区无血管区域的自动量化

浅表黄斑中心凹无血管区（sFAZ）的准确分割和量化对促进许多视网膜疾病（如糖尿病性视网膜病变和视网膜静脉阻塞）的诊断和治疗很重要。我们提出了一种基于深度学习的方法，用于对光学相干断层扫描血管造影（OCTA）图像中的sFAZ进行自动分割和量化，并且对亮度和对比度（B / C）变化具有鲁棒性。使用Zeiss Cirrus HD-OCT 5000获得了来自45位参与者的405张OCTA图像的数据集，随后对地面真相（GT）进行了手动分段。创建了具有编码器-解码器体系结构的深度学习网络，以将每个像素分类为sFAZ或非sFAZ类。后来，我们使用最大连接区域提取和孔填充来微调自动分割结果。当自动分割结果与GT进行比较时，获得的最大平均骰子相似系数（DSC）为0.976±0.011。根据自动分割结果计算出的面积与根据GT计算出的面积之间的相关系数为0.997。在所有具有不同亮度/对比度的九个参数组中，该方法的所有DSC均高于0.96。与之前报道的两种方法相比，该方法在sFAZ分割和量化方面实现了更好的性能。总之，我们提出并成功验证了基于深度学习且对B / C变化具有鲁棒性的自动sFAZ分割和量化方法。对于临床应用，