Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080, China.
Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
J Digit Imaging. 2023 Jun;36(3):1148-1157. doi: 10.1007/s10278-023-00786-0. Epub 2023 Feb 7.
Hyperreflective foci (HF) reflects inflammatory responses for fundus diseases such as diabetic macular edema (DME), retina vein occlusion (RVO), and central serous chorioretinopathy (CSC). Shown as high contrast and reflectivity in optical coherence tomography (OCT) images, automatic segmentation of HF in OCT images is helpful for the prognosis of fundus diseases. Previous traditional methods were time-consuming and required high computing power. Hence, we proposed a lightweight network to segment HF (with a speed of 57 ms per OCT image, at least 150 ms faster than other methods). Our framework consists of two stages: an NLM filter and patch-based split to preprocess images and a lightweight DBR neural network to segment HF automatically. Experimental results from 3000 OCT images of 300 patients (100 DME,100 RVO, and 100 CSC) revealed that our method achieved HF segmentation successfully. The DBR network had the area under curves dice similarity coefficient (DSC) of 83.65%, 76.43%, and 82.20% in segmenting HF in DME, RVO, and CSC on the test cohort respectively. Our DBR network achieves at least 5% higher DSC than previous methods. HF in DME was more easily segmented compared with the other two types. In addition, our DBR network is universally applicable to clinical practice with the ability to segment HF in a wide range of fundus diseases.
病理性高反射焦点（HF）反映了眼底疾病（如糖尿病性黄斑水肿（DME）、视网膜静脉阻塞（RVO）和中心性浆液性脉络膜视网膜病变（CSC））的炎症反应。在光学相干断层扫描（OCT）图像中，HF 表现为高对比度和高反射率，因此 OCT 图像中 HF 的自动分割有助于眼底疾病的预后。以前的传统方法既耗时又需要高计算能力。因此，我们提出了一种轻量级网络来分割 HF（每个 OCT 图像的速度为 57ms，比其他方法至少快 150ms）。我们的框架由两个阶段组成：一个 NLM 滤波器和基于补丁的分割来预处理图像，以及一个轻量级 DBR 神经网络来自动分割 HF。从 300 名患者的 3000 张 OCT 图像（100 名 DME、100 名 RVO 和 100 名 CSC）的实验结果表明，我们的方法成功地实现了 HF 分割。在测试队列中，DBR 网络在 DME、RVO 和 CSC 中分割 HF 的曲线下面积 DSC 分别为 83.65%、76.43%和 82.20%。与以前的方法相比，我们的 DBR 网络至少提高了 5%的 DSC。与其他两种类型相比，DME 中的 HF 更容易分割。此外，我们的 DBR 网络具有广泛的适用性，能够在各种眼底疾病中分割 HF。
