Liu Yu, Tang Zhenfei, Li Chao, Zhang Zhengwei, Zhang Yaqin, Wang Xiaogang, Wang Zhao
School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China.
Department of Ophthalmology, Wuxi No. 2 People's Hospital, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu 214002, China.
Biomed Opt Express. 2024 Oct 17;15(11):6416-6432. doi: 10.1364/BOE.534703. eCollection 2024 Nov 1.
Retinal vasculature is the only vascular system in the human body that can be observed in a non-invasive manner, with a phenotype associated with a wide range of ocular, cerebral, and cardiovascular diseases. OCT and OCT angiography (OCTA) provide powerful imaging methods to visualize the three-dimensional morphological and functional information of the retina. In this study, based on OCT and OCTA multimodal inputs, a multitask convolutional neural network model was built to realize 3D segmentation of retinal blood vessels and disease classification for different retinal diseases, overcoming the limitations of existing methods that can only perform 2D analysis of OCTA. Two hundred thirty sets of OCT and OCTA data from 109 patients, including 138,000 cross-sectional images in normal and diseased eyes (age-related macular degeneration, retinal vein occlusion, and central serous chorioretinopathy), were collected from four commercial OCT systems for model training, validation, and testing. Experimental results verified that the proposed method was able to achieve a DICE coefficient of 0.956 for 3D segmentation of blood vessels and an accuracy of 91.49% for disease classification, and further enabled us to evaluate the 3D reconstruction of retinal vessels, explore the interlayer connections of superficial and deep vasculatures, and reveal the 3D quantitative vessel characteristics in different retinal diseases.
视网膜血管系统是人体中唯一可以通过非侵入性方式观察到的血管系统，其表型与多种眼部、脑部和心血管疾病相关。光学相干断层扫描（OCT）和光学相干断层扫描血管造影（OCTA）提供了强大的成像方法，用于可视化视网膜的三维形态和功能信息。在本研究中，基于OCT和OCTA多模态输入，构建了一个多任务卷积神经网络模型，以实现视网膜血管的三维分割和不同视网膜疾病的分类，克服了现有方法只能对OCTA进行二维分析的局限性。从四个商用OCT系统收集了来自109名患者的230组OCT和OCTA数据，包括正常和患病眼睛（年龄相关性黄斑变性、视网膜静脉阻塞和中心性浆液性脉络膜视网膜病变）的138,000张横截面图像，用于模型训练、验证和测试。实验结果验证了所提出的方法在血管三维分割方面能够实现0.956的DICE系数，在疾病分类方面能够达到91.49%的准确率，并且进一步使我们能够评估视网膜血管的三维重建，探索浅表和深部血管系统的层间连接，并揭示不同视网膜疾病中的三维定量血管特征。
