Automated 3-D Retinal Layer Segmentation From SD-OCT Images With Neurosensory Retinal Detachment

Abstract

Neurosensory retinal detachment (NRD) is a separation of the neurosensory retina from the retinal pigment epithelium (RPE) because of the subretinal fluid that can result in significant vision loss. The detachment of the neurosensory retina is known to alter the topology as well as the intensity continuity of the retinal layers. This nature of NRD makes the layer segmentation of NRD affected eyes difficult. In this paper, we presented a fully automated three-dimensional (3D) method to segment the retinal layers and NRD associated subretinal fluid from a spectral domain optical coherence tomography (SD-OCT) image. The proposed method has three phases, including a prior information model; an NRD associated subretinal fluid segmentation; and layer segmentation. The graph search and graph cut techniques were employed to segment the retinal layers and NRD associated sub-retinal fluid, respectively. To reduce the computational cost of graph-based optimization, the `divide and merge' approach was introduced. The experiment shows that while maintaining the segmentation accuracy, the `divide and merge' approach considerably decreases the computational cost. Our method was evaluated on 20 SD-OCT cubes diagnosed with NRD, and the results were compared with the manual segmentation results from experts. The layer evaluation showed an overall absolute surface position difference of 6.34 ± 2.6μm, which is comparable with the inter-expert variability of 6.39 ± 5.9 μm. The segmentation result of the NRD associated sub-retinal fluid was assessed in terms of the dice coefficient and achieved means of 90.78% and 92.04% in comparison to two experts.

Keywords

• Graph cut
• graph optimization
• graph search
• neurosensory retinal detachment (NRD)
• retinal layer segmentation