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

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dc.authoridTuncer, Taner/0000-0003-0526-4526
dc.authorwosidTuncer, Taner/W-4789-2018
dc.authorwosidCankaya, Cem/HTR-3803-2023
dc.authorwosidCINAR, Ahmet/W-5792-2018
dc.contributor.authorFirat, Murat
dc.contributor.authorCinar, Ahmet
dc.contributor.authorCankaya, Cem
dc.contributor.authorFirat, Ilknur Tuncer
dc.contributor.authorTuncer, Taner
dc.date.accessioned2024-08-04T20:51:55Z
dc.date.available2024-08-04T20:51:55Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractKeratoconus 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.en_US
dc.identifier.doi10.1016/j.compbiomed.2022.105541
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid35525070en_US
dc.identifier.scopus2-s2.0-85129310978en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2022.105541
dc.identifier.urihttps://hdl.handle.net/11616/100628
dc.identifier.volume146en_US
dc.identifier.wosWOS:000800376200004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCorneal cross-linkingen_US
dc.subjectLinear interpolationen_US
dc.subject2D regressionen_US
dc.subjectDeep learningen_US
dc.subjectKeratoconusen_US
dc.titlePrediction of Pentacam image after corneal cross-linking by linear interpolation technique and U-NET based 2D regression modelen_US
dc.typeArticleen_US

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