Open AccessImproved cGAN based linear lesion segmentation in high myopia ICGA images
Author(s) -
Hongjiu Jiang,
Xinjian Chen,
Fei Shi,
Yuhui Ma,
Dehui Xiang,
Lei Ye,
Jinzhu Su,
Zuoyong Li,
Qiuying Chen,
Hua Yang,
Xun Xu,
Weifang Zhu,
Ying Fan
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.10.002355
Subject(s) - segmentation , artificial intelligence , computer science , generative adversarial network , deep learning , pattern recognition (psychology) , computer vision
The increasing prevalence of myopia has attracted global attention recently. Linear lesions including lacquer cracks and myopic stretch lines are the main signs in high myopia retinas, and can be revealed by indocyanine green angiography (ICGA). Automatic linear lesion segmentation in ICGA images can help doctors diagnose and analyze high myopia quantitatively. To achieve accurate segmentation of linear lesions, an improved conditional generative adversarial network (cGAN) based method is proposed. A new partial densely connected network is adopted as the generator of cGAN to encourage the reuse of features and make the network time-saving. Dice loss and weighted binary cross-entropy loss are added to solve the data imbalance problem. Experiments on our data set indicated that the proposed network achieved better performance compared to other networks.