Abstract
Optical coherence tomography is routinely used clinically for the detection and management of ocular diseases as well as in research where the studies may involve animals. This routine use requires that the developed automated segmentation methods not only be accurate and reliable, but also be adaptable to meet new requirements. We have previously proposed the use of a graph-theoretic approach for the automated 3-D segmentation of multiple retinal surfaces in volumetric human SD-OCT scans. The method ensures the global optimality of the set of surfaces with respect to a cost function. Cost functions have thus far been typically designed by hand by domain experts. This difficult and time-consuming task significantly impacts the adaptability of these methods to new models. Here, we describe a framework for the automated machine-learning based design of the cost function utilized by this graph-theoretic method. The impact of the learned components on the final segmentation accuracy are statistically assessed in order to tailor the method to specific applications. This adaptability is demonstrated by utilizing the method to segment seven, ten and five retinal surfaces from SD-OCT scans obtained from humans, mice and canines, respectively. The overall unsigned border position errors observed when using the recommended configuration of the graph-theoretic method was 6.45 ± 1.87 μm, 3.35 ± 0.62 μm and 9.75 ± 3.18 μm for the human, mouse and canine set of images, respectively.
Highlights
Spectral-domain optical coherence tomography (SD-OCT) imaging allows for the quantitative study of retinal structures, and finds widespread use in the detection and management of ocular diseases
Machine-learning based approaches are adapted to new models while graph-theoretic approaches are able to segment multiple surfaces simultaneously in 3-D while ensuring the global optimality of the solution with respect to a cost function
The machine-learning based automated design of the cost function allowed for the method to be quickly adapted to new models such as animal retinae as well as to the images obtained on a variety of scanners with varying imaging protocols
Summary
Spectral-domain optical coherence tomography (SD-OCT) imaging allows for the quantitative study of retinal structures, and finds widespread use in the detection and management of ocular diseases. The retinal nerve fiber layer (RNFL) and the ganglion cell layer (GCL) are not visually differentiable in mouse or canine SD-OCT images (see Fig. 1), unlike in human scans where the RNFL is quite distinct. This combined nerve fiber-ganglion cell (NF+GC) layer is significantly thinner in these animals than in humans Another distinct difference is the absence of the foveal depression in rodent and canine eyes. Prominent anatomical differences such as these make the direct application of methods developed for humans scans on animal scans prone to error. Automated methods developed need to be adaptable to new models while remaining robust to varying image resolutions as well as disease-induced disruptions that may be encountered
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