Incorporation of learned shape priors into a graph-theoretic approach with application to the 3D segmentation of intraretinal surfaces in SD-OCT volumes of mice

Spectral-domain optical coherence tomography (SD-OCT) finds widespread use clinically for the detection and management of ocular diseases. This non-invasive imaging modality has also begun to find frequent use in research studies involving animals such as mice. Numerous approaches have been proposed for the segmentation of retinal surfaces in SD-OCT images obtained from human subjects; however, the segmentation of retinal surfaces in mice scans is not as well-studied. In this work, we describe a graph-theoretic segmentation approach for the simultaneous segmentation of 10 retinal surfaces in SD-OCT scans of mice that incorporates learned shape priors. We compared the method to a baseline approach that did not incorporate learned shape priors and observed that the overall unsigned border position errors reduced from 3.58 +/- 1.33 μm to 3.20 +/- 0.56 μm.