Development of a semi-automatic segmentation method for retinal OCT images tested in patients with diabetic macular edema

Abstract

Purpose: To 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. Methods: SD-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. Results: The inter-grader reproducibility using the EdgeSelect method for retinal layers varied from 0.15 to 1.21 mm, smaller than those using the manual method (3.36-6.43 mm). 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 mm. 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). Conclusions: The 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. © 2013 Huang et al.