![]() Finally, while this paper was under review, a new work by Antony and colleagues was published which addressed a graph-based method for the automated segmentation of 10 retinal layer boundaries in normal mice, excluding the optic nerve head (ONH) region. 3) The algorithm only segmented six retinal layer boundaries, ignoring the NFL-GCL, IIS-OS, OIS-OS, and RPE-Choroid. 2) The test images were limited to the central slices of the volumes where the retinal layers were clearly visible, eliminating images from the periphery of the retinal volumes where retinal layers had lower quality and images of the optic nerve where several layers disappear. However, that paper was limited in application, as the test images were preselected based on three limiting criteria: 1) The test images were chosen from wild-type (WT) or diseased eyes in which no retinal layer was completely missing. The method by Yazdanpanah and colleagues utilizes active contours to segment retinal layers in SD-OCT images of rat eyes. These include a 3D segmentation algorithm by Ruggeri and colleagues that segments two retinal layer boundaries, and a two-algorithm method by Molnár and colleagues that segments three retinal layer boundaries by first calculating borders using row projections in a sliding window and then refining these borders iteratively. While many automated algorithms for segmenting retinal layers in human eyes have been developed, few have addressed the segmentation of murine eyes. Furthermore, layer thicknesses calculated from manual segmentations are inherently subjective due to variability between graders. However, segmenting these layers manually is time-consuming, limiting its practicality for use in large-scale studies. Accurate quantification of retinal layer thicknesses in spectral domain optical coherence tomography (SD-OCT) images of mouse eyes is crucial for the study and initial treatment evaluation of many ophthalmic and neurologic diseases in humans.
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