The presence of hyperreflective foci (HFs) is related to retinal disease progression, and the quantity has proven to be a prognostic factor of visual and anatomical outcome in various retinal diseases. However, lack of efficient quantitative tools for evaluating the HFs has deprived ophthalmologist of assessing the volume of HFs. For this reason, we propose an automated quantification algorithm to segment and quantify HFs in spectral domain optical coherence tomography (SD-OCT). The proposed algorithm consists of two parallel processes namely: region of interest (ROI) generation and HFs estimation. To generate the ROI, we use morphological reconstruction (MR) to obtain the reconstructed image and histogram constructed for data distributions and clustering. In parallel, we estimate the HFs by extracting the extremal regions from the connected regions obtained from a component tree. Finally, both the ROI and the HFs estimation process are merged to obtain the segmented HFs. The proposed algorithm was tested on 40 3D SD-OCT volumes from 40 patients diagnosed with non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME). The average dice similarity coefficient (DSC) and correlation coefficient (r) are 69.70%, 0.99 for NPDR, 70.31%, 0.99 for PDR, and 71.30%, 0.99 for DME respectively. The proposed algorithm can provide ophthalmologist with good HFs quantitative information, such as volume, size and location of the HFs.

中文翻译：

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糖尿病性视网膜病变的SD-OCT中高反射灶的自动定量

高反射病灶（HFs）的存在与视网膜疾病的进展有关，其数量已被证明是各种视网膜疾病的视觉和解剖结果的预后因素。然而，缺乏用于评估HF的有效定量工具，使得眼科医生无法评估HF的量。因此，我们提出了一种自动量化算法，用于对光谱域光学相干断层扫描（SD-OCT）中的HF进行分段和量化。所提出的算法包括两个并行过程，即：感兴趣区域（ROI）生成和HFs估计。为了生成ROI，我们使用形态重建（MR）来获取重建的图像和直方图，以构建数据分布和聚类。在平行下，我们通过从组成树获得的连接区域中提取末端区域来估计心力衰竭。最后，将ROI和HF估计过程合并，以获得分段的HF。在40例诊断为非增生性糖尿病性视网膜病（NPDR），增生性糖尿病性视网膜病（PDR）和糖尿病性黄斑水肿（DME）的患者中，对40种3D SD-OCT量进行了测试。平均骰子相似性系数（DSC）和相关系数（r）分别为NPDR的69.70％，0.99，PDR的70.31％，0.99和DME的71.30％，0.99。所提出的算法可以为眼科医生提供良好的HF定量信息，例如HF的体积，大小和位置。在40例诊断为非增生性糖尿病性视网膜病（NPDR），增生性糖尿病性视网膜病（PDR）和糖尿病性黄斑水肿（DME）的患者中，对40种3D SD-OCT量进行了测试。平均骰子相似性系数（DSC）和相关系数（r）分别为NPDR的69.70％，0.99，PDR的70.31％，0.99和DME的71.30％，0.99。所提出的算法可以为眼科医生提供良好的HF定量信息，例如HF的体积，大小和位置。在40例诊断为非增生性糖尿病性视网膜病（NPDR），增生性糖尿病性视网膜病（PDR）和糖尿病性黄斑水肿（DME）的患者中，对40种3D SD-OCT量进行了测试。平均骰子相似性系数（DSC）和相关系数（r）分别为NPDR的69.70％，0.99，PDR的70.31％，0.99和DME的71.30％，0.99。所提出的算法可以为眼科医生提供良好的HF定量信息，例如HF的体积，大小和位置。DME分别为99。所提出的算法可以为眼科医生提供良好的HF定量信息，例如HF的体积，大小和位置。DME分别为99。所提出的算法可以为眼科医生提供良好的HF定量信息，例如HF的体积，大小和位置。