Unified deep learning model for predicting fundus fluorescein angiography image from fundus structure image

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Yiwei Chen, Yi He, Hong Ye, Lina Xing, Xin Zhang, Guohua Shi
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引用次数: 0

Abstract

The prediction of fundus fluorescein angiography (FFA) images from fundus structural images is a cutting-edge research topic in ophthalmological image processing. Prediction comprises estimating FFA from fundus camera imaging, single-phase FFA from scanning laser ophthalmoscopy (SLO), and three-phase FFA also from SLO. Although many deep learning models are available, a single model can only perform one or two of these prediction tasks. To accomplish three prediction tasks using a unified method, we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network. The three prediction tasks are processed as follows: data preparation, network training under FFA supervision, and FFA image prediction from fundus structure images on a test set. By comparing the FFA images predicted by our model, pix2pix, and CycleGAN, we demonstrate the remarkable progress achieved by our proposal. The high performance of our model is validated in terms of the peak signal-to-noise ratio, structural similarity index, and mean squared error.

从眼底结构图像预测眼底荧光素血管造影图像的统一深度学习模型
从眼底结构图像预测眼底荧光素血管造影(FFA)图像是眼科图像处理领域的前沿研究课题。预测包括通过眼底照相机成像估算 FFA、通过扫描激光眼底镜(SLO)估算单相 FFA 和通过 SLO 估算三相 FFA。虽然目前有许多深度学习模型,但单一模型只能完成其中的一两项预测任务。为了用一种统一的方法完成三项预测任务,我们提出了一种统一的深度学习模型,利用有监督的生成对抗网络从眼底结构图像中预测 FFA 图像。三项预测任务的处理过程如下:数据准备、FFA 监督下的网络训练以及在测试集上从眼底结构图像预测 FFA 图像。通过比较我们的模型、pix2pix 和 CycleGAN 预测的 FFA 图像,我们证明了我们的建议所取得的显著进步。我们模型的高性能在峰值信噪比、结构相似性指数和均方误差方面都得到了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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