Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation

Abstract

Optical Coherence Tomography (OCT) is one of the most informative methodologies in ophthalmology and provides cross sectional images from anterior and posterior segments of the eye. Corneal diseases can be diagnosed by these images and corneal thickness maps can also assist in the treatment and diagnosis. The need for automatic segmentation of cross sectional images is inevitable since manual segmentation is time consuming and imprecise. In this paper, segmentation methods such as Gaussian Mixture Model (GMM), Graph Cut, and Level Set are used for automatic segmentation of three clinically important corneal layer boundaries on OCT images. Using the segmentation of the boundaries in three-dimensional corneal data, we obtained thickness maps of the layers which are created by these borders. Mean and standard deviation of the thickness values for normal subjects in epithelial, stromal, and whole cornea are calculated in central, superior, inferior, nasal, and temporal zones (centered on the center of pupil). To evaluate our approach, the automatic boundary results are compared with the boundaries segmented manually by two corneal specialists. The quantitative results show that GMM method segments the desired boundaries with the best accuracy.

Accurate tissue interface segmentation via adversarial pre-segmentation of anterior segment OCT images

“…(a),(d) Original B-scans from a 4×4mm limbal dataset acquired using a hand-held SD-OCT scanner and from a 3×3mm corneal dataset acquired using a UHR-OCT scanner respectively. As proposed in previous algorithms [5,7,11,[64][65][66][67][68][69][70][71], vertical lines (magenta) denote the division of the image into three regions in order to deal with specular artifacts. (b),(e) Segmentation of the shallowest interface (cyan contour) by these algorithms failed due to presence of specular artifacts in different regions in the image.…”

“…However, among all the aforementioned methods, the majority of traditional methods [40,41,[43][44][45][46][47][48][49] and learning-based methods [57][58][59][60][61][62][63]72] are focused on retinal interface segmentation. Corneal interface segmentation algorithms are predominately based on traditional approaches [5,7,11,[64][65][66][67][68][69][70][71], with limited learning-based approaches [29,73] being proposed. Similarly, prior work on limbal interface segmentation is limited to a traditional image analysisbased approach [12].…”

“…As seen in Figs. 2(b) and 2(e), previous segmentation approaches would divide (A-scan-wise) the OCT image into three sections, and assume that the location of the central specular artifact was limited to the center of the OCT image (region between the vertical magenta lines) [5,7,11,[64][65][66][67][68][69][70][71]. But as seen in Fig.…”

“…The other approach is our recent publication [29], where we utilized the entire OCT sequence from multiple scanners containing strong specular artifacts and low SNR regions, and successfully segmented three tissue interfaces. Yet, our previously proposed approach did not readily provide intermediate outputs, wherein the specular artifacts and speckle noise patterns were ameliorated, which could be used as input to the traditional approaches [5,7,11,12,[64][65][66][67][68][69][70][71] for segmentation.…”

Objective quantification of corneal haziness using anterior segment optical coherence tomography

“…One of the early attempts to measure light scatter in vivo was based on the use of an optical fiber, Scheimpflug photography, video pachometry using a slit-lamp, confocal microscopy and OCT. 9 Various techniques have been used for segmentation of three important layers of the cornea in normal eyes using AS-OCT. 10 …”