Khin Yadanar Win, Jipson Wong Hon Fai, Wong Qiu Ying, Chloe Chua Si Qi, Jacqueline Chua, Damon Wong, Marcus Ang, Leopold Schmetterer, Bingyao Tan
{"title":"Corneal Layer Segmentation in Healthy and Pathological Eyes: A Joint Super-Resolution Generative Adversarial Network and Adaptive Graph Theory Approach.","authors":"Khin Yadanar Win, Jipson Wong Hon Fai, Wong Qiu Ying, Chloe Chua Si Qi, Jacqueline Chua, Damon Wong, Marcus Ang, Leopold Schmetterer, Bingyao Tan","doi":"10.1167/tvst.14.3.19","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To enhance corneal layer segmentation and thickness measurement in ultra-high axial resolution optical coherence tomography (OCT) images for both healthy and pathological eyes using super-resolution generative adversarial network and adaptive graph theory.</p><p><strong>Methods: </strong>We combine a super-resolution generative adversarial network (SRGAN) with adaptive graph theory for an improved segmentation accuracy of five corneal layers: epithelium, Bowman's, corneal stroma, Descemet's membrane, and endothelium. The fine-tuned SRGAN enhances the contrast and visibility of layer interfaces, particularly Descemet's membrane. For the layer segmentation with graph theory, search spaces were adapted according to the contrasts of the layers. We segmented volumetric high-resolution corneal OCT images of healthy participants, patients who underwent Descemet's membrane endothelial keratoplasty (DMEK), and patients with Fuchs endothelial corneal dystrophy (FECD).</p><p><strong>Results: </strong>Enface thickness maps were generated over a 4-mm field of view from both healthy and pathological eyes. The measurements showed high reproducibility (intraclass correlation coefficient [ICC] = 0.97) for the whole cornea and stroma and moderate reproducibility for the other layers (ICC = 0.64 for epithelium/Bowman's complex; ICC = 0.53 for endothelium/Descemet's membrane complex). The average thickness errors were 3.5 µm for the total cornea, 4.4 µm for epithelium, 2.5 µm for Bowman's, 4.3 µm for stroma, and 3.0 µm for endothelium/Descemet's membrane complex.</p><p><strong>Conclusions: </strong>The proposed method consistently outperforms conventional graph search methods across all corneal layer segmentations, which is beneficial for diagnosing and monitoring corneal diseases.</p><p><strong>Translational relevance: </strong>Our method can provide precise thickness measurement of multiple corneal layers, which has the potential to improve DMEK monitoring and FECD diagnosis.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 3","pages":"19"},"PeriodicalIF":2.6000,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11932425/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational Vision Science & Technology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1167/tvst.14.3.19","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
Corneal Layer Segmentation in Healthy and Pathological Eyes: A Joint Super-Resolution Generative Adversarial Network and Adaptive Graph Theory Approach.
Purpose: To enhance corneal layer segmentation and thickness measurement in ultra-high axial resolution optical coherence tomography (OCT) images for both healthy and pathological eyes using super-resolution generative adversarial network and adaptive graph theory.
Methods: We combine a super-resolution generative adversarial network (SRGAN) with adaptive graph theory for an improved segmentation accuracy of five corneal layers: epithelium, Bowman's, corneal stroma, Descemet's membrane, and endothelium. The fine-tuned SRGAN enhances the contrast and visibility of layer interfaces, particularly Descemet's membrane. For the layer segmentation with graph theory, search spaces were adapted according to the contrasts of the layers. We segmented volumetric high-resolution corneal OCT images of healthy participants, patients who underwent Descemet's membrane endothelial keratoplasty (DMEK), and patients with Fuchs endothelial corneal dystrophy (FECD).
Results: Enface thickness maps were generated over a 4-mm field of view from both healthy and pathological eyes. The measurements showed high reproducibility (intraclass correlation coefficient [ICC] = 0.97) for the whole cornea and stroma and moderate reproducibility for the other layers (ICC = 0.64 for epithelium/Bowman's complex; ICC = 0.53 for endothelium/Descemet's membrane complex). The average thickness errors were 3.5 µm for the total cornea, 4.4 µm for epithelium, 2.5 µm for Bowman's, 4.3 µm for stroma, and 3.0 µm for endothelium/Descemet's membrane complex.
Conclusions: The proposed method consistently outperforms conventional graph search methods across all corneal layer segmentations, which is beneficial for diagnosing and monitoring corneal diseases.
Translational relevance: Our method can provide precise thickness measurement of multiple corneal layers, which has the potential to improve DMEK monitoring and FECD diagnosis.
期刊介绍：
Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO.
The journal covers a broad spectrum of work, including but not limited to:
Applications of stem cell technology for regenerative medicine,
Development of new animal models of human diseases,
Tissue bioengineering,
Chemical engineering to improve virus-based gene delivery,
Nanotechnology for drug delivery,
Design and synthesis of artificial extracellular matrices,
Development of a true microsurgical operating environment,
Refining data analysis algorithms to improve in vivo imaging technology,
Results of Phase 1 clinical trials,
Reverse translational ("bedside to bench") research.
TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.
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