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Spectral-domain optical coherence tomography (SD-OCT) is a non-invasive imaging modality that allows for the quantitative study of retinal structures. SD-OCT has begun to find widespread use in the diagnosis and management of various ocular diseases. While commercial scanners provide limited analysis of a small number of retinal layers, the automated segmentation of retinal layers and other structures within these volumetric images is quite a challenging problem, especially in the presence of disease-induced changes.
The incorporation of a priori information, ranging from qualitative assessments of the data to automatically learned features, can significantly improve the performance of automated methods. Here, a combined machine learning-based approach and graph-theoretic approach is presented for the automated segmentation of retinal structures in SD-OCT images. Machine-learning based approaches are used to learn textural features from a training set, which are then incorporated into the graph- theoretic approach. The impact of the learned features on the final segmentation accuracy of the graph-theoretic approach is carefully evaluated so as to avoid incorporating learned components that do not improve the method. The adaptability of this versatile combination of a machine-learning and graph-theoretic approach is demonstrated through the segmentation of retinal surfaces in images obtained from humans, mice and canines.
In addition to this framework, a novel formulation of the graph-theoretic approach is described whereby surfaces with a disruption can be segmented. By incorporating the boundary of the "hole" into the feasibility definition of the set of surfaces, the final result consists of not only the surfaces but the boundary of the hole as well. Such a formulation can be used to model the neural canal opening (NCO) in SD-OCT images, which appears as a 3-D planar hole disrupting the surfaces in its vicinity. A machine-learning based approach was also used here to learn descriptive features of the NCO.
Thus, the major contributions of this work include 1) a method for the automated correction of axial artifacts in SD-OCT images, 2) a combined machine-learning and graph-theoretic framework for the segmentation of retinal surfaces in SD-OCT images (applied to humans, mice and canines), 3) a novel formulation of the graph-theoretic approach for the segmentation of multiple surfaces and their shared hole (applied to the segmentation of the neural canal opening), and 4) the investigation of textural markers that could precede structural and functional change in degenerative retinal diseases.