The correct segmentation of blood vessels in optical coherence tomography (OCT) images may be an important requirement for the analysis of intra-retinal layer thickness in human retinal diseases. We developed a shape model based procedure for the automatic segmentation of retinal blood vessels in spectral domain (SD)-OCT scans acquired with the Spectralis OCT system. The segmentation procedure is based on a statistical shape model that has been created through manual segmentation of vessels in a training phase. The actual segmentation procedure is performed after the approximate vessel position has been defined by a shadowgraph that assigns the lateral vessel positions. The active shape model method is subsequently used to segment blood vessel contours in axial direction. The automated segmentation results were validated against the manual segmentation of the same vessels by three expert readers. Manual and automated segmentations of 168 blood vessels from 34 B-scans were analyzed with respect to the deviations in the mean Euclidean distance and surface area. The mean Euclidean distance between the automatically and manually segmented contours (on average 4.0 pixels respectively 20 µm against all three experts) was within the range of the manually marked contours among the three readers (approximately 3.8 pixels respectively 18 µm for all experts). The area deviations between the automated and manual segmentation also lie within the range of the area deviations among the 3 clinical experts. Intra reader variability for the experts was between 0.9 and 0.94. We conclude that the automated segmentation approach is able to segment blood vessels with comparable accuracy as expert readers and will provide a useful tool in vessel analysis of whole C-scans, and in particular in multicenter trials.
METHODS. The algorithm is based on the segmentation of the gray-level intensities within a Bscan by a k-means cluster analysis and subsequent classification by a k-nearest neighbor algorithm. Accuracy was evaluated against three clinical experts using 130 bullous cavities identified on eight SD-OCT B-scans of three patients with wet age-related macular degeneration (AMD) and five patients with X-linked retinoschisis, as well as on one volume scan of a patient with X-linked retinoschisis. The algorithm calculated the surface area of the cavities for the B-scans and the volume of all cavities for the volume scan. In order to validate the applicability of the algorithm in clinical use, we analyzed 31 volume scans taken over the course of 4 years for one AMD patient with a serous retinal detachment.RESULTS. Discrepancies in area measurements between the segmentation results of the algorithm and the experts were within the range of the area deviations among the experts. Volumes interpolated from the B-scan series of the volume scan were comparable among experts and algorithm (0.249 mm 3 for the algorithm, 0.271 mm 3 for expert 1, 0.239 mm 3 for expert 2, and 0.262 mm 3 for expert 3). Volume changes of the serous retinal detachment were quantifiable. CONCLUSIONS.The segmentation algorithm represents a method for the automated analysis of large numbers of volume scans during routine diagnostics and in clinical trials.
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