Commercially-available optical coherence tomography (OCT) systems (e.g., Stratus OCT-3) only segment and
provide thickness measurements for the total retina on scans of the macula. Since each intraretinal layer may be
affected differently by disease, it is desirable to quantify the properties of each layer separately. Thus, we have
developed an automated segmentation approach for the separation of the retina on (anisotropic) 3-D macular
OCT scans into five layers. Each macular series consisted of six linear radial scans centered at the fovea. Repeated
series (up to six, when available) were acquired for each eye and were first registered and averaged together,
resulting in a composite image for each angular location. The six surfaces defining the five layers were then found
on each 3-D composite image series by transforming the segmentation task into that of finding a minimum-cost
closed set in a geometric graph constructed from edge/regional information and a priori-determined surface
smoothness and interaction constraints. The method was applied to the macular OCT scans of 12 patients with
unilateral anterior ischemic optic neuropathy (corresponding to 24 3-D composite image series). The boundaries
were independently defined by two human experts on one raw scan of each eye. Using the average of the experts'
tracings as a reference standard resulted in an overall mean unsigned border positioning error of 6.7 ± 4.0
&mgr;m, with five of the six surfaces showing significantly lower mean errors than those computed between the two
observers (p < 0.05, pixel size of 50 × 2 &mgr;m).