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.
Highlights
In corneal Spectral domain optical coherence tomography (SDOCT) images, there are two main types of artifacts that often interfere with accurate automatic segmentation
Each volume consisted of 50 radial by a selected number of neighboring vertical columns (A-scans) in the lateral (B-scan), 1000 A-scans each, and 1024 axial pixels per A-scan
In this work, we presented an automatic method for accurate segmentation of three clinically important corneal layer boundaries on SDOCT images of normal eyes
Summary
Spectral domain optical coherence tomography (SDOCT) has become an important diagnostic imaging modality in clinical ophthalmology [1,2,3,4] for the examination of both the retina [1,2,17,18,19,20,21,28] and cornea [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,25,26,27,29]. The large volume of data generated from imaging in settings such as busy clinics or large-scale clinical trials makes manual segmentation both impractical and costly for the analysis of corneal SDOCT images To address this issue, several different approaches for segmenting corneal layer boundaries have been proposed with varying levels of success. The hybrid graph theory and dynamic programming retinal segmentation approach introduced by Chiu et al has been shown to be especially flexible for handling different sources of artifacts [21]. This robust segmentation method is capable of handling varying degrees of SNR and artifacts in corneal images.
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