Corneal Layer Segmentation in Healthy and Pathological Eyes: A Joint Super-Resolution Generative Adversarial Network and Adaptive Graph Theory Approach.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY

Translational Vision Science & Technology Pub Date : 2025-03-03 DOI:10.1167/tvst.14.3.19

Khin Yadanar Win, Jipson Wong Hon Fai, Wong Qiu Ying, Chloe Chua Si Qi, Jacqueline Chua, Damon Wong, Marcus Ang, Leopold Schmetterer, Bingyao Tan

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引用次数: 0

Abstract

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.

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目的：利用超分辨率生成对抗网络和自适应图论，提高健康眼和病变眼超高轴向分辨率光学相干断层扫描（OCT）图像中角膜层的分割和厚度测量：我们将超分辨率生成对抗网络（SRGAN）与自适应图论相结合，提高了五层角膜的分割精度：上皮、鲍曼角膜、角膜基质、德斯梅尔膜和内皮。经过微调的 SRGAN 增强了角膜层界面的对比度和可见度，尤其是 Descemet 膜。在利用图论进行图层分割时，我们根据图层的对比度调整了搜索空间。我们对健康参与者、接受德斯梅尔膜内皮角膜移植术（DMEK）的患者和福氏内皮性角膜营养不良症（FECD）患者的高分辨率角膜 OCT 图像进行了体积分割：在健康眼和病变眼的 4 毫米视野范围内生成了角膜厚度图。测量结果显示，整个角膜和基质层的重现性很高（类内相关系数 [ICC] = 0.97），其他各层的重现性一般（上皮/鲍曼复合体的 ICC = 0.64；内皮/Descemet's 膜复合体的 ICC = 0.53）。总角膜的平均厚度误差为 3.5 微米，上皮细胞为 4.4 微米，鲍曼复合体为 2.5 微米，基质层为 4.3 微米，内皮细胞/脱落膜复合体为 3.0 微米：结论：所提出的方法在所有角膜层的分割上都优于传统的图搜索方法，有利于诊断和监测角膜疾病：我们的方法可以精确测量多个角膜层的厚度，有望改善 DMEK 监测和 FECD 诊断。

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

Translational Vision Science & Technology Engineering-Biomedical Engineering

CiteScore

5.70

自引率

3.30%

发文量

346

审稿时长

25 weeks

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.