Abstract
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.
Highlights
Optical coherence tomography (OCT) has revolutionized the in vivo imaging capacities in ophthalmology
The actual segmentation procedure was performed on unseen images by creating a shadowgraph of the scan to assign the lateral vessel position followed by the active shape model (ASM) method to segment the vessels in vertical direction
The above described algorithm for automated blood vessel segmentation in B-scans taken with a Spectralis OCT device was evaluated for accuracy and reliability against three expert readers
Summary
Optical coherence tomography (OCT) has revolutionized the in vivo imaging capacities in ophthalmology. The technique uses low coherence infrared light to visualize the reflectance of retinal structures in depth, similar to ultrasound. Since the early days about 20 years ago, technical advances in image quality and acquisition have made this technique crucial in the diagnostic procedure of many retinal and optic nerve disorders. In contrast to the image quality of modern devices, image analysis tools are still quite restricted. Most commercial OCT devices currently perform only the segmentation of the entire retina or segmentation of the retinal nerve fiber layer (RNFL). It is of note though that the high resolution of SD-OCT scans theoretically allows for the analysis of more structures, including all discernible intra-retinal layers, blood vessels, druses, and fluid filled regions. A meaningful analysis requires the identification and segmentation of all of these structures in the same OCT B-scan
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call