Identifying the retinal layers from optical coherence tomography images using a 3D segmentation method

Abstract A novel automated method for segmenting retinal layers in three‐dimensional (3D) space from spectral domain optical coherence tomography (SD‐OCT) images. Compared to 2D segmentation, 3D segmentation uses more data and produces findings that are more accurate and reliable. The class‐specific area of interest (ROI) choice and three important reference class approximations make the suggested technique precise, effective, and reliable. In the first step, contours are detected based on gradient intensity. To choose a smaller region of interest (ROI), the second stage entails acquiring the identified boundary neighbour B scan data for the selected ROI by categorising the problem as a graph problem. The third stage involves locating edge pixels using Canny Edge Detection from nodes. In order to calculate the edge weight of a histogram, slope similarity to the reference line and node characteristics are considered. The fourth phase boundary is precisely found by Dijkstra's shortest path algorithm. The accuracy of the method was tested based on 288 B scans of 12 patients (ten normal macular degeneration (AMD) subjects and 2 age‐related subjects from two different institutions). Five recent automated procedures are compared with the results to further validate the findings of the fifth phase. The outcomes demonstrate a mean original mean square error (RMSE) for each of the cut‐off values, which are 2.82, 4.88, 2.03, 3.77, and 0.64 pixels, respectively. As can be seen, the suggested strategy outperforms the existing models' significantly with a return on investment of 0.26 pixels.