Model-Driven 3-D Regularisation for Robust Segmentation of the Refractive Corneal Surfaces in Spiral OCT Scans

Wagner, Joerg; Pezold, Simon; Cattin, Philippe C.

doi:10.1007/978-3-319-67561-9_12

Cited by 1 publication

(2 citation statements)

References 8 publications

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“… 11 – 13 , 15 – 26 In these methods, the corneal layer boundaries are modeled using circles, 22 parabolas, 11 – 13 , 16 , 17 , 19 fourth-order polynomials, 15 , 18 ellipses, 24 , 25 or Zernike polynomials. 23 Some of these methods first extract the highest intensity points of each boundary from the OCT image or its gradient and fit them to a model to estimate the boundary. 11 , 13 , 15 , 17 , 22 The accuracy of these methods depends on the quality of the OCT images and they are most likely to fail in images with low signal to noise ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

“…In other methods, instead of extracting the highest intensity points, graph theory and dynamic programming or level sets are used to search for the minimum-cost paths in the image, according to a designed cost function, which is more likely to pass through the boundary. 12 , 19 , 21 , 23 , 25 , 26 However, when segmenting the corneal OCT images with low SNR, a model is used with these methods to approximate or limit the search region of each boundary in low-SNR regions.…”

Section: Introductionmentioning

confidence: 99%

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Pathological-Corneas Layer Segmentation and Thickness Measurement in OCT Images

Elsawy

Gregori

Eleiwa

et al. 2020

Trans. Vis. Sci. Tech.

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Purpose The purpose of this study was to propose a new algorithm for the segmentation and thickness measurement of pathological corneas with irregular layers using a two-stage graph search and ray tracing. Methods In the first stage, a graph, with only gradient edge-cost, is used to segment the air-epithelium and endothelium-aqueous boundaries. In the second stage, a graph, with gradient, directional, and multiplier edge-cost, is used to correct segmentation. The optical coherence tomography (OCT) image is flattened using the air-epithelium boundary and a graph search is used to segment the epithelium-Bowman's and Bowman's-stroma boundaries. Then, the OCT image is flattened using the endothelium-aqueous boundary and a graph search is used to segment the Descemet's membrane. Ray tracing is used to correct the inter-boundary distances, then the thickness is measured using the shortest distance. The proposed algorithm was trained and evaluated using 190 OCT images manually segmented by trained operators. Results The mean and standard deviation of the unsigned errors of the algorithm-operator and inter-operator were 0.89 ± 1.03 and 0.77 ± 0.68 pixels in segmentation and 3.62 ± 3.98 and 2.95 ± 2.52 µm in thickness measurement. Conclusions Our proposed algorithm can produce accurate segmentation and thickness measurements compared with the manual operators. Translational Relevance Our algorithm could be potentially useful in the clinical practice.

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