Choroidal Optical Coherence Tomography Angiography:Noninvasive Choroidal Vessel Analysis via Deep Learning  

作　　者：Lei Zhu Junmeng Li Yicheng Hu Ruilin Zhu Shuang Zeng Pei Rong Yadi Zhang Xiaopeng Gu Yuwei Wang Zhiyue Zhang Liu Yang Qiushi Ren Yanye Lu 

机构地区：[1]Institute of Medical Technology,Peking University Health Science Center,Peking University,Beijing 100191,China [2]Department of Biomedical Engineering,Peking University,Beijing 100871,China [3]Department of Ophthalmology,Peking University First Hospital,Beijing 100034,China [4]National Biomedical Imaging Center,Peking University,Beijing 100871,China

出　　处：《Health Data Science》2024年第1期136-151,共16页健康数据科学(英文)

基　　金：supported in part by the Natural Science Foundation of China under grants 82371112,623B2001,and 62394311;in part by the Beijing Natural Science Foundation under grant Z210008;in part by the High-grade,Precision and Advanced University Discipline Construction Project of Beijing(BMU2024GJJXK004).

摘　　要：Background:The choroid is the most vascularized structure in the human eye, associated with numerous retinal and choroidal diseases. However, the vessel distribution of choroidal sublayers has yet to be effectively explored due to the lack of suitable tools for visualization and analysis. Methods:In this paper, we present a novel choroidal angiography strategy to more effectively evaluate vessels within choroidal sublayers in the clinic. Our approach utilizes a segmentation model to extract choroidal vessels from OCT B-scans layer by layer. Furthermore, we ensure that the model, trained on B-scans with high choroidal quality, can proffciently handle the low-quality B-scans commonly collected in clinical practice for reconstruction vessel distributions. By treating this process as a cross-domain segmentation task, we propose an ensemble discriminative mean teacher structure to address the speciffcities inherent in this cross-domain segmentation process. The proposed structure can select representative samples with minimal label noise for self-training and enhance the adaptation strength of adversarial training. Results:Experiments demonstrate the effectiveness of the proposed structure, achieving a dice score of 77.28 for choroidal vessel segmentation. This validates our strategy to provide satisfactory choroidal angiography noninvasively, supportting the analysis of choroidal vessel distribution for paitients with choroidal diseases. We observed that patients with central serous chorioretinopathy have evidently (P < 0.05) lower vascular indexes at all choroidal sublayers than healthy individuals, especially in the region beyond central fovea of macula (larger than 6 mm). Conclusions:We release the code and training set of the proposed method as the ffrst noninvasive mechnism to assist clinical application for the analysis of choroidal vessels.

关 键 词：utilize HANDLE DEEP 

分 类 号：H31[语言文字—英语]