A Generative Model for OCT Retinal Layer Segmentation by Group wise Curve Alignment

Abstract

Retinal layer segmentation from optical coherence tomography (OCT) is of fundamental importance for measuring retinal layer thicknesses. These thickness measurements have been shown to correlate well with the severity of different ocular diseases; hence, they provide useful diagnostic information concerning diseases. Manual segmentation of retinal layers from OCT remains dominant in ophthalmological clinical practice but has serious drawbacks: it is time consuming, labor intensive, and results in inter/intrarater variations. Computer aided segmentation has attracted intensive research attention because it holds the potential not only to provide repeatable, quantitative, and objective results but also to reduce the time and effort required to delineate the retinal layers. However, most of the existing computer based retinal layer segmentation techniques focus on segmenting specific layers by exploring their unique characteristics; thus, they can fail to segment a retinal layer that is totally different. In this paper, we propose a generative retinal layer segmentation method based on groupwise curve alignment that combines the capabilities of segmenting different retinal layers into a unified framework. This method is unique for both its accuracy and its ability to segment any retinal layer without any special modifications. We experimentally validate that the proposed method outperforms a representative state-of-the-art technique by using images of both normal healthy eyes and diseased eyes. Our method is potentially useful in a large variety of practical applications involving retinal layer segmentation from OCT.