Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming

Abstract

Segmentation of anatomical structures in corneal images is crucial for the diagnosis and study of anterior segment diseases. However, manual segmentation is a time-consuming and subjective process. This paper presents an automatic approach for segmenting corneal layer boundaries in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Our approach is robust to the low-SNR and different artifact types that can appear in clinical corneal images. We show that our method segments three corneal layer boundaries in normal adult eyes more accurately compared to an expert grader than a second grader—even in the presence of significant imaging outliers.

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

“…Mathematic model based methods construct a fixed or adaptive model based on prior assumptions for the structure of the input images, and include A-scan [16,17], active contour [18][19][20][21], sparse high order potentials [22], and 2D/3D graph [23][24][25][26][27][28][29][30] based methods. Machine learning based methods formulate layer segmentation as a classification problem, where features are extracted from each layer or its boundaries and used to train a classifier (e.g.…”

Handheld, rapidly switchable, anterior/posterior segment swept source optical coherence tomography probe

“…OCT B-scan images were registered using an image processing program, ImageJ (National Institutes of Health, Bethesda, MD), with the StackReg plugin [16]. OCT volumes were registered by applying previously described automatic segmentation algorithms using graph theory and dynamic programming [17,18] to extract the top layer of the retina or cornea followed by cross correlation of the segmentations to determine axial motion. Fig.…”

Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology

“…In this section, we briefly review the layers of the murine retina, the GTDP framework originally developed for human retinal [4,5,18] and corneal [19] layer segmentation, as well as the basics of SVM classification [20]. While the general GTDP framework is similar for different applications, in the following we have modified and extended the core formula in the context of murine retinal layer boundary segmentation.…”