An Automated Approach for Inner Segment/Outer Segment Defect Detection in Retinal SD-OCT Images

There has been shown a strong association between the integrity of the inner/outer segment (IS/OS) junction and the visual acuity in various retinal diseases. We propose an automated method for IS/OS defect detection in optical coherence tomography images with a focus on resilience, particularly with epiretinal membranes present.50 eyes with epiretinal membranes were included in this study, and received retinal scans were acquired using spectral domain optical coherence tomography. The algorithm is based on the pixel value maxima above the retinal pigment epithelium. Summing up the pixel values row-wise to the lower part of the image leads to two maxima with respect to the IS and OS layer. The classification algorithm itself is divided into two parts, the polynomial interpolation and the Gabor filtering. Performing both parts yield to the classification IS/OS defect. In order to quantify the algorithmic performance of the proposed method, the resulting classification was compared against corresponding gold standard data. Algorithmically classified pixels were compared to this gold standard. The algorithm reached a mean performance of 96.77% sensitivity and 99.09% specificity. We demonstrate that the automatic classification of IS/OS defects in optical coherence tomography is possible with high sensitivity and specificity.

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An Automated Approach for Inner Segment/Outer Segment Defect Detection in Retinal SD-OCT Images