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PURPOSE: To develop a software algorithm to perform automated segmentation of retinal layer structures on linear macular optical coherence tomography (StratusOCT; Carl Zeiss Meditec, Inc., Dublin, CA) scan images and to test its performance in discriminating normal from glaucomatous eyes in comparison with conventional circumpapillary nerve fiber layer (cpNFL) thickness measurement. METHODS: Four layer structures within the retina were defined: the macular nerve fiber layer (mNFL), the inner retinal complex (IRC; retinal ganglion cell RGC layer + inner plexiform and nuclear layers), outer plexiform layer (OPL), and outer retinal complex (ORC; outer nuclear layer + photoreceptor layer). Normal and glaucomatous eyes underwent fast macular map and fast NFL OCT scans. Linear macular images were analyzed using the developed algorithm, and the results were compared with the cpNFL thickness measurement. RESULTS: Forty-seven subjects (23 normal and 24 with glaucoma) were analyzed. mNFL, cpNFL, IRC, and the total retinal thicknesses were significantly greater in normal than in glaucomatous eyes (P or = 0.15). CONCLUSIONS: The newly developed macular segmentation algorithm described herein demonstrated its ability to quantify objectively the glaucomatous damage to RGCs and NFL and to discriminate between glaucomatous and normal eyes. Further algorithm refinement and improvements in resolution and image quality may yield a more powerful methodology for clinical glaucoma evaluation.
Ishikawa et al. (Tue,) studied this question.