Development of a Semi-Automatic Segmentation Method for Retinal OCT Images Tested in Patients with Diabetic Macular Edema

Abstract

PurposeTo develop EdgeSelect, a semi-automatic method for the segmentation of retinal layers in spectral domain optical coherence tomography images, and to compare the segmentation results with a manual method.MethodsSD-OCT (Heidelberg Spectralis) scans of 28 eyes (24 patients with diabetic macular edema and 4 normal subjects) were imported into a customized MATLAB application, and were manually segmented by three graders at the layers corresponding to the inner limiting membrane (ILM), the inner segment/ellipsoid interface (ISe), the retinal/retinal pigment epithelium interface (RPE), and the Bruch's membrane (BM). The scans were then segmented independently by the same graders using EdgeSelect, a semi-automated method allowing the graders to guide/correct the layer segmentation interactively. The inter-grader reproducibility and agreement in locating the layer positions between the manual and EdgeSelect methods were assessed and compared using the Wilcoxon signed rank test.ResultsThe inter-grader reproducibility using the EdgeSelect method for retinal layers varied from 0.15 to 1.21 µm, smaller than those using the manual method (3.36–6.43 µm). The Wilcoxon test indicated the EdgeSelect method had significantly better reproducibility than the manual method. The agreement between the manual and EdgeSelect methods in locating retinal layers ranged from 0.08 to 1.32 µm. There were small differences between the two methods in locating the ILM (p = 0.012) and BM layers (p<0.001), but these were statistically indistinguishable in locating the ISe (p = 0.896) and RPE layers (p = 0.771).ConclusionsThe EdgeSelect method resulted in better reproducibility and good agreement with a manual method in a set of eyes of normal subjects and with retinal disease, suggesting that this approach is feasible for OCT image analysis in clinical trials.

Biomechanical, ultrastructural, and electrophysiological characterization of the non-human primate experimental glaucoma model

“…Evaluation of scans focused on the RNFL, retinal ganglion cell layer (RNGL), optic nerve head (ONH), and macula. Segmentation and determination of RNFL thickness (RNFLT) was performed using standard automated algorithms as well as manual and combined (EdgeSelect™) techniques 66 . S cotopic and photopic full-field ERGs (ffERG) to a series of flash strengths, photopic flash visual-evoked potentials (fVEP), pattern electroretinograms (PERG) and pattern reversal visual evoked potentials (PRVEP), in that order.…”

Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema

“…We expect that these parameters will provide valuable prognostic information to guide treatment decisions. However, despite the development of automated retinal layer segmentation methods applicable to normal eyes or eyes with limited pathological deformation or loss of retinal structures [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42], most DME studies require either manual [22,[24][25][26]43] or semi-automatic [44] evaluation of OCT images. Currently few automated algorithms exist to quantify morphological or pathological features on images with DME [43,[45][46][47][48][49][50][51].…”

Automated Internal Classification of Beadless Chinese ZhuJi Freshwater Pearls based on Optical Coherence Tomography Images

“…The added nodes and weights made it possible to assign any nodes in left added row as start point and those in right added row as end point, and the boundary lateral passed the whole image through edges with minimum sum of weights. We assigned the middle vertical points in two added rows as start and end point, and used the Dijkstra’s algorithm (one of the classic graph approach) to find the boundaries 29 .…”