Prediction of Pentacam image after corneal cross-linking by linear interpolation technique and U-NET based 2D regression model

Küçük Resim Yok

Tarih

2022

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Pergamon-Elsevier Science Ltd

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Keratoconus is a common corneal disease that causes vision loss. In order to prevent the progression of the disease, the corneal cross-linking (CXL) treatment is applied. The follow-up of keratoconus after treatment is essential to predict the course of the disease and possible changes in the treatment. In this paper, a deep learningbased 2D regression method is proposed to predict the postoperative Pentacam map images of CXL-treated patients. New images are obtained by the linear interpolation augmentation method from the Pentacam images obtained before and after the CXL treatment. Augmented images and preoperative Pentacam images are given as input to U-Net-based 2D regression architecture. The output of the regression layer, the last layer of the U-Net architecture, provides a predicted Pentacam image of the later stage of the disease. The similarity of the predicted image in the final layer output to the Pentacam image in the postoperative period is evaluated by image similarity algorithms. As a result of the evaluation, the mean SSIM (The structural similarity index measure), PSNR (peak signal-to-noise ratio), and RMSE (root mean square error) similarity values are calculated as 0.8266, 65.85, and 0.134, respectively. These results show that our method successfully predicts the postoperative images of patients treated with CXL.

Açıklama

Anahtar Kelimeler

Corneal cross-linking, Linear interpolation, 2D regression, Deep learning, Keratoconus

Kaynak

Computers in Biology and Medicine

WoS Q Değeri

Q1

Scopus Q Değeri

Q1

Cilt

146

Sayı

Künye