An Innovative 3D Adaptive Patient-Related Atlas for Automatic Segmentation of Retina Layers from Oct Images

Abstract

This paper introduces a 3D segmentation approach using an adaptive patient-specific retinal atlas and an appearance model for 3D Optical Coherence Tomography (OCT) data. In order to reconstruct the 3D patient-specific retinal atlas, we started by segmenting the macula central area where the fovea is clearly identified in the data to be segmented. The segmentation of this selected foveal area inside the retina is accomplished by using joint Markov Gibbs Random Field (MGRF) integrating shape, intensity, and spatial information of 12 retinal layers. A 2D shape prior was built using a series of co-registered training OCT images that were collected from 200 different subjects. The shape prior was then adapted to the first order appearance and second order spatial interaction MGRF model of the data to be segmented. Once the middle of the macula “foveal area” had been segmented, its segmented layers' labels and their appearances were used to segment the adjacent slices. The previous step was propagated until the complete 3D OCT patient-data was segmented. The proposed approach was tested on 30 different subjects, with either normal or pathological OCT scans, and then compared with a delineated ground truth and the results were then verified by retina specialists. Performance was measured using the Dice Similarity Coefficient (DSC), agreement coefficient (AC), and average deviation (AD) metrics. The accuracy achieved by the segmentation approach clearly demonstrates the promise of the proposed segmentation approach and shows improvement over a state-of-the-art 3D OCT segmentation approach currently in use.