Conference Proceeding
Authors: Wenxiang Deng (University of Iowa) , Bhavna Antony (University of Iowa) , Elliott H. Sohn (University of Iowa) , Michael D. Abràmoff (University of Iowa) , Mona K. Garvin (The University of Iowa, Iowa City)
Automated approaches for the segmentation of the retinal vessels are helpful for longitudinal studies of mice using spectral-domain optical coherence tomography (SD-OCT). In the SD-OCT volumes of human eyes, the retinal vasculature can be readily visualized by creating a projected average intensity image in the depth direction. The created projection images can then be segmented using standard approaches. However, in the SD-OCT volumes of mouse eyes, the creation of projection images from the entire volume typically results in very poor images of the vasculature. The purpose of this work is to present and evaluate three machine-learning approaches, namely baseline, single-projection, and all-layers approaches, for the automated segmentation of retinal vessels within SD-OCT volumes of mice. Twenty SD-OCT volumes (400 × 400 × 1024 voxels) from the right eyes of twenty mice were obtained using a Bioptigen SD-OCT machine (Morrisville, NC) to evaluate our methods. The area under the curve (AUC) for the receiver operating characteristic (ROC) curves of the all-layers approach, 0.93, was significantly larger than the AUC for the single-projection (0.91) and baseline (0.88) approach with p < 0.05.
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How to Cite: Deng, W. , Antony, B. , Sohn, E. H. , Abràmoff, M. D. & Garvin, M. K. (2015) “Segmentation of the Retinal Vasculature within Spectral-Domain Optical Coherence Tomography Volumes of Mice”, Proceedings of the Ophthalmic Medical Image Analysis International Workshop. 2(2015). doi: https://doi.org/10.17077/omia.1028