Okuwobi Idowu Paul, Fan Wen, Yu Chenchen, Yuan Songtao, Liu Qinghuai, Zhang Yuhan, Loza Bekalo, Chen Qiang
Nanjing University of Science and Technology, School of Computer Science and Engineering, Xiaolingwei, Nanjing, China.
The First Affiliated Hospital with Nanjing Medical University, Department of Ophthalmology, Nanjing, China.
J Med Imaging (Bellingham). 2018 Jan;5(1):014002. doi: 10.1117/1.JMI.5.1.014002. Epub 2018 Feb 6.
We propose an automated segmentation method to detect, segment, and quantify hyperreflective foci (HFs) in three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT). The algorithm is divided into three stages: preprocessing, layer segmentation, and HF segmentation. In this paper, a supervised classifier (random forest) was used to produce the set of boundary probabilities in which an optimal graph search method was then applied to identify and produce the layer segmentation using the Sobel edge algorithm. An automated grow-cut algorithm was applied to segment the HFs. The proposed algorithm was tested on 20 3-D SD-OCT volumes from 20 patients diagnosed with proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME). The average dice similarity coefficient and correlation coefficient ([Formula: see text]) are 62.30%, 96.90% for PDR, and 63.80%, 97.50% for DME, respectively. The proposed algorithm can provide clinicians with accurate quantitative information, such as the size and volume of the HFs. This can assist in clinical diagnosis, treatment, disease monitoring, and progression.
我们提出了一种自动分割方法，用于在三维（3-D）光谱域光学相干断层扫描（SD-OCT）中检测、分割和量化高反射灶（HFs）。该算法分为三个阶段：预处理、层分割和HF分割。在本文中，使用监督分类器（随机森林）生成边界概率集，然后应用最优图搜索方法，利用Sobel边缘算法识别并生成层分割。应用自动生长切割算法分割HFs。所提出的算法在来自20例诊断为增殖性糖尿病视网膜病变（PDR）和糖尿病性黄斑水肿（DME）患者的20个3-D SD-OCT容积上进行了测试。PDR的平均骰子相似系数和相关系数（[公式：见正文]）分别为62.30%、96.90%，DME的分别为63.80%、97.50%。所提出的算法可以为临床医生提供准确的定量信息，如HFs的大小和体积。这有助于临床诊断、治疗、疾病监测和病情进展。
