ArticleTranslational vision science & technology2020
Level-Set Method for Image Analysis of Schlemm's Canal and Trabecular Meshwork.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Automated quantification of parafoveal microvascular complexity using deep learning-based OCTA segmentation and foveal avascular zone detection.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Machine-Learning-Based Automated Schlemm's Canal Volumetric Segmentation for Optical Coherence Tomography.Chemical & biomedical imaging · 2026Article
- Quantitative analysis of anterior and posterior chamber parameters of primary angle-closure glaucoma based on level set method.BMC ophthalmology · 2026Article
- Molecular and Biomechanical Changes in the Anterior Segment of High Myopic Eyes.Annals of biomedical engineering · 2026Review
- Open-source deep learning-based automatic segmentation of mouse Schlemm's canal in optical coherence tomography images.Experimental eye research · 2022Article
- Full circumferential morphological analysis of Schlemm's canal in human eyes using megahertz swept source OCT.Biomedical optics express · 2021Article
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5 authors.
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Abstract
Purpose: To evaluate different segmentation methods in analyzing Schlemm's canal (SC) and the trabecular meshwork (TM) in ultrasound biomicroscopy (UBM) images. Methods: Twenty-six healthy volunteers were recruited. The intraocular pressure (IOP) was measured while study subjects blew a trumpet. Images were obtained at different IOPs by 50-MHz UBM. ImageJ software and three segmentation methods- Results: A total of 104 UBM images were obtained. Among them, 84 were adequately clear to be segmented. The level-set method results had a higher similarity to ImageJ results than the other two methods. The ICC values of the level-set method were 0.97, 0.95, 0.9, and 0.57, respectively. Pearson correlation coefficients for the IOP to the SC area, SC perimeter, SC length, and TM width were -0.91, -0.72, -0.66, and -0.61 ( Conclusions: The level-set method showed better accuracy than the other two methods. Compared with manual methods, it can achieve similar precision, better repeatability, and greater efficiency. Therefore, the level-set method can be used for reliable UBM image segmentation. Translational Relevance: The level-set method can be used to analyze TM and SC region in UBM images semiautomatically.
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