Department of Ophthalmology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China.
College of Information Science, Shanghai Ocean University, Shanghai 201306, China.
Biomed Res Int. 2021 Feb 17;2021:6679556. doi: 10.1155/2021/6679556. eCollection 2021.
Optical coherence tomography (OCT) provides the visualization of macular edema which can assist ophthalmologists in the diagnosis of ocular diseases. Macular edema is a major cause of vision loss in patients with retinal vein occlusion (RVO). However, manual delineation of macular edema is a laborious and time-consuming task. This study proposes a joint model for automatic delineation of macular edema in OCT images. This model consists of two steps: image enhancement using a bioinspired algorithm and macular edema segmentation using a Gaussian-filtering regularized level set (SBGFRLS) algorithm. We then evaluated the delineation efficiency using the following parameters: accuracy, precision, sensitivity, specificity, Dice's similarity coefficient, IOU, and kappa coefficient. Compared with the traditional level set algorithms, including C-V and GAC, the proposed model had higher efficiency in macular edema delineation as shown by reduced processing time and iteration times. Moreover, the accuracy, precision, sensitivity, specificity, Dice's similarity coefficient, IOU, and kappa coefficient for macular edema delineation could reach 99.7%, 97.8%, 96.0%, 99.0%, 96.9%, 94.0%, and 96.8%, respectively. More importantly, the proposed model had comparable precision but shorter processing time compared with manual delineation. Collectively, this study provides a novel model for the delineation of macular edema in OCT images, which can assist the ophthalmologists for the screening and diagnosis of retinal diseases.
光学相干断层扫描（OCT）可提供黄斑水肿的可视化图像，有助于眼科医生诊断眼部疾病。黄斑水肿是视网膜静脉阻塞（RVO）患者视力下降的主要原因。然而，黄斑水肿的手动描绘是一项繁琐且耗时的任务。本研究提出了一种用于 OCT 图像中黄斑水肿自动描绘的联合模型。该模型由两步组成：使用仿生算法进行图像增强和使用高斯滤波正则化水平集（SBGFRLS）算法进行黄斑水肿分割。然后，我们使用以下参数评估描绘效率：准确性、精度、敏感性、特异性、Dice 相似系数、IOU 和kappa 系数。与传统的水平集算法（包括 C-V 和 GAC）相比，所提出的模型在黄斑水肿描绘方面效率更高，表现为处理时间和迭代次数减少。此外，黄斑水肿描绘的准确性、精度、敏感性、特异性、Dice 相似系数、IOU 和 kappa 系数分别可达 99.7%、97.8%、96.0%、99.0%、96.9%、94.0%和 96.8%。更重要的是，与手动描绘相比，所提出的模型具有可比的精度和更短的处理时间。总之，本研究为 OCT 图像中黄斑水肿的描绘提供了一种新的模型，可协助眼科医生进行视网膜疾病的筛查和诊断。
