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

<div><p>Purpose</p><p>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.</p><p>Methods</p><p>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.</p><p>Results</p><p>The 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).</p><p>Conclusions</p><p>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.</p></div

Mean, standard deviation, and concordance correlation of the inter-grader reproducibility of the EdgeSelect and the Manual methods.

<p>Layers are: inner limiting membrane (ILM), inner segment/ellipsoid interface (ISe), retina/retinal pigment epithelium interface (RPE), and Bruch's membrane (BM).</p

Agreement in segmented layer locations between the manual and EdgeSelect methods.

<p>Solid lines indicate the mean difference of the agreement between the two methods and the dashed lines indicate mean ± 2 standard deviations. The filled symbols are data points from patients and the open symbols are from normal subjects. ILM: inner limiting membrane; ISe: the inner-segment/ellipsoid interface; RPE: the retinal/retinal pigment epithelium interface; BM: the Bruch's membrane.</p

Comparison of the inter-grader reproducibility between the manual and the EdgeSelect methods in all 28 data samples.

<p>The filled symbols are data points from patients and the open symbols are from normal subjects. ILM: inner limiting membrane; ISe: the inner-segment/ellipsoid interface; RPE: the retinal/retinal pigment epithelium interface; BM: the Bruch's membrane.</p

A representative OCT image with layers segmented using EdgeSelect.

<p>(<b>A</b>) OCT images were captured with horizontal raster lines covering 4.5×6 mm area centered at the fovea. (<b>B</b>) A representative B scan is shown by the red line in (A). The inner limiting membrane (ILM), the inner-segment/ellipsoid interface (ISe), the retinal/retinal pigment epithelium interface (RPE), and the Bruch's membrane (BM), were segmented.</p

Graphic representation of segmenting the inner limiting membrane (ILM) interface in a representative B scan using the EdgeSelect method.

<p>(<b>A</b>) A representative OCT B scan. (<b>B</b>) Detection of image contrast change using zero-crossing of a Laplacian-of-Gaussian (LoG) filter. (<b>C</b>) The detected edges are assigned different weights based on intensity/gradient characteristics. Higher intensity represents larger weight, corresponding to strong edges; lower intensity signals are assigned lesser weight corresponding to weak edges. (<b>D</b>) An edge candidate map (blue lines) is generated using a Canny-like filtering scheme, and is superimposed on the original OCT image. (<b>E</b>) The start and end edge candidates are initiated, and the path of the shortest distance via Dijkstra's algorithm (red line) is computed. (<b>F</b>) Human grader intervention adds additional seed edges, and the program regenerates automatically the updated shortest path until the proper layer segmentation is reached.</p