Automated segmentation of 3-D spectral OCT retinal blood vessels by neural canal opening false positive suppression

Authors:
Zhihong Hu;Meindert Niemeijer;Michael D. Abrà//moff;Kyungmoo Lee;Mona K. Garvin
Affiliations:
Departments of Electrical and Computer Engineering, and The University of Iowa, Iowa City, IA;Departments of Electrical and Computer Engineering, and The University of Iowa, Iowa City, IA and Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA;Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA and Departments of Electrical and Computer Engineering, and The University of Iowa, Iowa City, IA and Iowa City VA Medical ...;Departments of Electrical and Computer Engineering, and The University of Iowa, Iowa City, IA;Departments of Electrical and Computer Engineering, and The University of Iowa, Iowa City, IA
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

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Abstract

We present a method for automatically segmenting the blood vessels in optic nerve head (ONH) centered spectral-domain optical coherence tomography (SD-OCT) volumes, with a focus on the ability to segment the vessels in the region near the neural canal opening (NCO). The algorithm first pre-segments the NCO using a graph-theoretic approach. Oriented Gabor wavelets rotated around the center of the NCO are applied to extract features in a 2-D vessel-aimed projection image. Corresponding oriented NCO-based templates are utilized to help suppress the false positive tendency near the NCO boundary. The vessels are identified in a vessel-aimed projection image using a pixel classification algorithm. Based on the 2-D vessel profiles, 3-D vessel segmentation is performed by a triangular-mesh-based graph search approach in the SD-OCT volume. The segmentation method is trained on 5 and is tested on 10 randomly chosen independent ONH-centered SD-OCT volumes from 15 subjects with glaucoma. Using ROC analysis, for the 2-D vessel segmentation, we demonstrate an improvement over the closest previous work with an area under the curve (AUC) of 0.81 (0.72 for previously reported approach) for the region around the NCO and 0.84 for the region outside the NCO (0.81 for previously reported approach).