Automated segmentation of retinal nonperfusion area in fluorescein angiography in retinal vein occlusion using convolutional neural networks

摘要

Purpose Retinal vein occlusion (RVO) is the second most common cause of vision loss after diabetic retinopathy due to retinal vascular disease. Retinal nonperfusion (RNP), identified on fluorescein angiograms (FA) and appearing as hypofluorescence regions, is one of the most significant characteristics of RVO. Quantification of RNP is crucial for assessing the severity and progression of RVO. However, in current clinical practice, it is mostly conducted manually, which is time-consuming, subjective, and error-prone. The purpose of this study is to develop fully automated methods for segmentation of RNP using convolutional neural networks (CNNs).