Zebrafish have been identified as an ideal model for angiogenesis because of anatomical and functional similarities with
other vertebrates. The scale and complexity of zebrafish assays are limited by the need to manually treat and serially screen
animals, and recent technological advances have focused on automation and improving throughput. Here, we use optical
coherence tomography (OCT) and OCT angiography (OCT-A) to perform noninvasive, in vivo imaging of retinal
vasculature in zebrafish. OCT-A summed voxel projections were low pass filtered and skeletonized to create an en face
vascular map prior to connectivity analysis. Vascular segmentation was referenced to the optic nerve head (ONH), which
was identified by automatically segmenting the retinal pigment epithelium boundary on the OCT structural volume. The
first vessel branch generation was identified as skeleton segments with branch points closest to the ONH, and subsequent
generations were found iteratively by expanding the search space outwards from the ONH. Biometric parameters,
including length, curvature, and branch angle of each vessel segment were calculated and grouped by branch generation.
Despite manual handling and alignment of each animal over multiple time points, we observe distinct qualitative patterns
that enable unique identification of each eye from individual animals. We believe this OCT-based retinal biometry method
can be applied for automated animal identification and handling in high-throughput organism-level pharmacological
assays and genetic screens. In addition, these extracted features may enable high-resolution quantification of longitudinal
vascular changes as a method for studying zebrafish models of retinal neovascularization and vascular remodeling.
Ivan Bozic, Gopikrishna M. Rao, Vineet Desai, and Yuankai K. Tao, "Automated feature extraction for retinal vascular biometry in zebrafish using OCT angiography," Proc. SPIE 10045, Ophthalmic Technologies XXVII, 100450J (Presented at SPIE BiOS: January 28, 2017; Published: 8 February 2017); https://doi.org/10.1117/12.2252370.
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