Postoperative corneal topography generation based on attention mechanism and Pix2Pix network

Abstract

AIM:To explore the use of attention mechanism and Pix2Pix generative adversarial network to predict the postoperative corneal topography of age-related cataract patients undergone femtosecond laser arcuate keratotomy.METHODS:In this retrospective case series study, the 210 preoperative and postoperative corneal topographies from 87 age-related cataract patients(105 eyes)undergoing femtosecond laser arcuate keratotomy at Shanxi Eye Hospital between March 2018 and March 2020 were selected and divided into a training set(180)and a test set(30)for model training and testing. The peak signal-to-noise ratio(PSNR), structural similarity(SSIM)and Alpins astigmatism vector analysis were used to compare the accuracy of postoperative corneal topography prediction under different attention mechanisms.RESULTS:The model based on attention mechanism and Pix2Pix network can predict postoperative corneal topography, among which the model based on Self-Attention mechanism has the best prediction effect, with PSNR and SSIM reaching 16.048 and 0.7661, respectively. There were no statistically significant differences in the difference vector, difference vector axis position, surgically induced astigmatism, and correction index between real and generated corneal topography on the 3mm and 5mm rings(all P>0.05).CONCLUSION:Based on the Self-Attention mechanism and Pix2Pix network, the postoperative corneal topography can be well predicted, which can provide reference for the surgical planning and postoperative effects of ophthalmic clinicians.

Keywords

• pix2pix network
• generative adversarial network
• attention mechanism
• corneal topography
• deep learning