The latest generation of spectral optical coherence tomography (OCT) scanners is able to image 3D cross-sectional volumes of the retina at a high resolution and high speed. These scans offer a detailed view of the structure of the retina. Automated segmentation of the vessels in these volumes may lead to more objective diagnosis of retinal vascular disease including hypertensive retinopathy, retinopathy of prematurity. Additionally, vessel segmentation can allow color fundus images to be registered to these 3D volumes, possibly leading to a better understanding of the structure and localization of retinal structures and lesions. In this paper we present a method for automatically segmenting the vessels in a 3D OCT volume. First, the retina is automatically segmented into multiple layers, using simultaneous segmentation of their boundary surfaces in 3D. Next, a 2D projection of the vessels is produced by only using information from certain segmented layers. Finally, a supervised, pixel classification based vessel segmentation approach is applied to the projection image. We compared the influence of two methods for the projection on the performance of the vessel segmentation on 10 optic nerve head centered 3D OCT scans. The method was trained on 5 independent scans. Using ROC analysis, our proposed vessel segmentation system obtains an area under the curve of 0.970 when compared with the segmentation of a human observer.
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).