Translational vision science & technology · 2025
Corneal Layer Segmentation in Healthy and Pathological Eyes: A Joint Super-Resolution Generative Adversarial Network and Adaptive Graph Theory Approach.
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Win Khin Yadanar, Fai Jipson Wong Hon, Ying Wong Qiu, Qi Chloe Chua Si, Chua Jacqueline, Wong Damon, Ang Marcus, Schmetterer Leopold, Tan Bingyao
Аннотация
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.
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.