A generative model for OCT retinal layer segmentation by integrating graph-based multi-surface searching and image registration.

We proposed a generative probabilistic modeling framework for automated segmentation of retinal layers from Optical Coherence Tomography (OCT) data. The objective is to learn a segmentation protocol from a collection of training images that have been manually labeled. Our model results in a novel OCT retinal layer segmentation approach which integrates algorithms of simultaneous searching of multiple interacting layer interfaces, image registration and machine learning. Different from previous work, our approach combines the benefits of constraining spatial layout of retinal layers, using a set of more robust local image descriptors, employing a mechanism for learning from manual labels and incorporating the inter-subject anatomical similarities of retina. With a set of OCT volumetric images from mutant canine retinas, we experimentally validated that our approach outperforms two state-of-the-art techniques.