Abstract
Investigations in how the retinal microvasculature correlates with ophthalmological conditions necessitate a method for measuring the microvasculature. Optical coherence tomography angiography (OCTA) depicts the superficial and the deep layer of the retina, but quantification of the microvascular network is still needed. Here, we propose an automatic quantitative analysis of the retinal microvasculature. We use a dictionary-based segmentation to detect larger vessels and capillaries in the retina and we extract features such as densities and vessel radius. The method is validated on repeated OCTA scans from healthy subjects, and we observe high intraclass correlation coefficients and high agreement in a Bland-Altman analysis. The quantification method is also applied to pre- and postoperative scans of cataract patients. Here, we observe a higher variation between the measurements, which can be explained by the greater variation in scan quality. Statistical tests of both the healthy subjects and cataract patients show that our method is able to differentiate subjects based on the extracted microvascular features.
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Acknowledgements
We would like to thank Professor Emeritus Knut Conradsen, DTU Compute, and Assistant Professor Anders Nymark, DTU Compute, for valuable assistance and guidance in the statistical analysis.
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Engberg, A.M.E., Erichsen, J.H., Sander, B., Kessel, L., Dahl, A.B., Dahl, V.A. (2020). Automated Quantification of Retinal Microvasculature from OCT Angiography Using Dictionary-Based Vessel Segmentation. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_22
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DOI: https://doi.org/10.1007/978-3-030-39343-4_22
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