A Deep Learning Framework for Identifying Zone I in RetCam Images

• Main Authors: Jinfeng Zhao, Baiying Lei, Zhenquan Wu, Yinsheng Zhang, Yafeng Li, Li Wang, Ruyin Tian, Yi Chen, Dahui Ma, Jiantao Wang, Tianfu Wang, Guozhen Chen, Jian Zeng, Guoming Zhang
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Retinopathy of prematurity; deep convolutional neural network; optic disc; macular center; zone I
• Online Access: https://ieeexplore.ieee.org/document/8767920/

Retinopathy of prematurity (ROP) has been one of the worldwide causes of blindness among children. Grading and treatment guidelines of ROP are mainly based on zone, stage, and plus disease. For serious ROP, identifying zone is more important than staging. However, identifying zone I from RetCam fundus images is not accurate and subjective by ophthalmologists. To address it, we develop a new deep learning framework to automatically identify zone I from RetCam images. Specifically, we train a deep convolutional neural network (DCNN) algorithm based on the RetCam images. The disc and macular center in terms of the threshold of intersection over union (IOU) were identified automatically. The algorithm is validated on fundus images and results show that zone I identification accuracy of 91% is achieved when the IOU threshold is 0.8. The obtained promising identification accuracy of zone I from the RetCam images indicates the potential applications in ROP grading, monitoring, and prognosis for infants.