Retinal Nerve Fiber Layer Segmentation on FD-OCT Scans of Normal Subjects and Glaucoma Patients

Abstract

Automated measurements of the retinal nerve fiber layer thickness on circular OCT B-Scans provide physicians additional parameters for glaucoma diagnosis. We propose a novel retinal nerve fiber layer segmentation algorithm for frequency domain data that can be applied on scans from both normal healthy subjects, as well as glaucoma patients, using the same set of parameters. In addition, the algorithm remains almost unaffected by image quality. The main part of the segmentation process is based on the minimization of an energy function consisting of gradient and local smoothing terms. A quantitative evaluation comparing the automated segmentation results to manually corrected segmentations from three reviewers is performed. A total of 72 scans from glaucoma patients and 132 scans from normal subjects, all from different persons, composed the database for the evaluation of the segmentation algorithm. A mean absolute error per A-Scan of 2.9 µm was achieved on glaucomatous eyes, and 3.6 µm on healthy eyes. The mean absolute segmentation error over all A-Scans lies below 10 µm on 95.1% of the images. Thus our approach provides a reliable tool for extracting diagnostic relevant parameters from OCT B-Scans for glaucoma diagnosis.

Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images

“…However, we emphasize that the independent application of this algorithm is not the most appropriate method for the detection of all diseases with retinal manifestations. For example, the detection of the earliest stages of diabetic retinopathy [26] or glaucoma [16] is expected to be significantly more accurate when using layer segmentation methods. We expect that the most efficient fully automated remote diagnostic system for ophthalmic diseases would incorporate both of these approaches.…”

“…Over the past two decades, the application of image processing and computer vision to OCT image interpretation has mostly focused on the development of automated retinal layer segmentation methods [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Segmented layer thicknesses are compared to the corresponding thickness measurements from normative databases to help identify retinal diseases [19][20][21][22].…”

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.…”

Multiple-object geometric deformable model for segmentation of macular OCT

“…These eight layers (and their associated nine boundaries) are the maximal set that are typically segmented from OCT of the macular retina. There has been a large body of work on macular retinal OCT layer segmentation [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. A diverse array of approaches have been investigated including methods based on active contours [19,20] [31], registration [34], and level sets [35].…”

“…There has been a large body of work on macular retinal OCT layer segmentation [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. A diverse array of approaches have been investigated including methods based on active contours [19,20] [31], registration [34], and level sets [35]. None of these methods, with the exception of the the active contours [19, 20] method of Ghorbel et al and the "loosely coupled level sets" (LCLC) method of Novosel et al [35], define boundaries between retinal layers in a subvoxel manner.…”