Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

Abstract

Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.

Decision Trees for Glaucoma Screening Based on the Asymmetry of the Retinal Nerve Fiber Layer in Optical Coherence Tomography

“…For this purpose, new asymmetry metrics have been defined for the thickness of the RNFL obtained from OCTs of both eyes of the same patient. In this work, the mean thickness of each sector of the RNFL was provided by the segmentation of Spectralis; however, segmentation methods such as those described in [30,41] will be used in the future to address the relationship of the RNFL thickness at each angle of the B-scan in the diagnosis of glaucoma.…”

“…The relations between polar coordinates measured in degrees and Cartesian coordinates measured in pixels is provided in Table 1 and outlined at the bottom of Figure 1. As described in [30], expert ophthalmologists diagnosed both eyes of all individuals in the database using three possible levels of glaucoma disease: healthy, not healthy and uncertain. Given the aim of this work to compare the asymmetry between the eyes of healthy and glaucoma patients, only individuals with two healthy eyes (160 individuals) and with both eyes with glaucoma disease (47 individuals) were selected (see Table 2).…”

“…In addition, since the RNFL is the innermost layer of the retina, artifacts due to the shadowing effect of the retinal veins appear in the images, increasing the difficulty of precisely defining the layers in specific sectors of the eye. The most commonly used approaches for this segmentation are comprehensively reviewed in [30], and some of them are relevant to the dataset used here.…”

Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning

“…Fewer works have approached the problem of segmenting the layers of the retina in glaucoma patients, attending to the disease-specific degeneration that affects them. Even then, these are limited to a single scan pattern, namely the circumpapillary scan (28)(29)(30)(31) or the ONH (32), while foregoing the analysis of the other relevant views for the diagnosis of glaucoma. In this sense, to the best of our knowledge, the segmentation of the relevant retinal structures for the diagnosis of glaucoma in all of the OCT views used for its assessment remains to be addressed.…”

Updates in Clinical and Translational Glaucoma Research

“…Throughout the last decade, the interest in glaucoma diagnosis and therapy has been encouraged through outstanding biotechnological advances and the emergence of artificial intelligence to make the decision-making processes of glaucoma management easier [ 2 , 3 ]. Clinicians, biomedical engineers, and scientific researchers have been involved in improving knowledge of glaucoma risk factors and pathogenic mechanisms as well as refining innovative tools for glaucoma diagnostic performance, such as those wielded for corneal biomechanical properties, intraocular pressure (IOP) measurement, structural and functional glaucoma probes [ 4 , 5 , 6 , 7 , 8 , 9 ], and those regarding the discovery of IOP-lowering medical, laser, and surgical approaches [ 10 , 11 ].…”