Fundus tessellation segmentation and quantization based on the deep convolution neural network

Zhen Guo; Lingzhi Chen; Lilong Wang; Chuanfeng Lyu; Guotong Xie; Yan Gao; Jun LI

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Fundus tessellation segmentation and quantization based on the deep convolution neural network

VernacularTitle:基于深度卷积神经网络的眼底豹纹分割量化及应用
Author: Zhen GUO ¹ ; Lingzhi CHEN ; Lilong WANG ; Chuanfeng LYU ; Guotong XIE ; Yan GAO ; Jun LI
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1. 山东第一医科大学附属青岛眼科医院　山东省眼科研究所　山东省眼科学重点实验室-省部共建国家重点实验室培育基地,　青岛　266071
Keywords: Myopia; Neural networks (computer); Color fundus image; Tessellation density
From: Chinese Journal of Ocular Fundus Diseases 2022;38(2):114-119
CountryChina
Language:Chinese
Abstract: Objective:To propose automatic measurement of global and local tessellation density on color fundus images based on a deep convolutional neural network (DCNN) method.Methods:An applied study. An artificial intelligence (AI) database was constructed, which contained 1 005 color fundus images captured from 1 024 eyes of 514 myopic patients in the Northern Hospital of Qingdao Eye Hospital from May to July, 2021. The images were preprocessed by using RGB color channel re-calibration method (CCR algorithm), CLAHE algorithm based on Lab color space, Retinex algorithm for multiple iterative illumination estimation, and multi-scale Retinex algorithm. The effects on the segmentation of tessellation by adopting the abovemetioned image enhancement methods and utilizing the Dice, Edge Overlap Rate and clDice loss were compared and observed. The tessellation segmentation model for extracting the tessellated region in the full fundus image as well as the tissue detection model for locating the optic disc and macular fovea were built up. Then, the fundus tessellation density (FTD), macular tessellation density (MTD) and peripapillary tessellation density (PTD) were calculated automatically.Results:When applying CCR algorithm for image preprocessing and the training losses combination strategy, the Dice coefficient, accuracy, sensitivity, specificity and Jordan index for fundus tessellation segmentation were 0.723 4, 94.25%, 74.03%, 96.00% and 70.03%, respectively. Compared with the manual annotations, the mean absolute errors and root mean square errors of FTD, MTD, PTD automatically measured by the model were 0.014 3, 0.020 7, 0.026 7 and 0.017 8, 0.032 3, 0.036 5, respectively.Conclusion:The DCNN-based segmentation and detection method can automatically measure the tessellation density in the global and local regions of the fundus of myopia patients, which can more accurately assist clinical monitoring and evaluation of the impact of fundus tessellation changes on the development of myopia.