Enhanced visualization of the retinal vasculature using depth information in OCT
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Enhanced visualization of the retinal vasculature using depth information in OCTDate
2017-06-17Citation
de Moura, J., Novo, J., Charlón, P. et al. Enhanced visualization of the retinal vasculature using depth information in OCT. Med Biol Eng Comput 55, 2209–2225 (2017). https://doi.org/10.1007/s11517-017-1660-8
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https://doi.org/10.1007/s11517-017-1660-8
Abstract
[Abstract]: Retinal vessel tree extraction is a crucial step for analyzing the microcirculation, a frequently needed process in the study of relevant diseases. To date, this has normally been done by using 2D image capture paradigms, offering a restricted visualization of the real layout of the retinal vasculature. In this work, we propose a new approach that automatically segments and reconstructs the 3D retinal vessel tree by combining near-infrared reflectance retinography information with Optical Coherence Tomography (OCT) sections. Our proposal identifies the vessels, estimates their calibers, and obtains the depth at all the positions of the entire vessel tree, thereby enabling the reconstruction of the 3D layout of the complete arteriovenous tree for subsequent analysis. The method was tested using 991 OCT images combined with their corresponding near-infrared reflectance retinography. The different stages of the methodology were validated using the opinion of an expert as a reference. The tests offered accurate results, showing coherent reconstructions of the 3D vasculature that can be analyzed in the diagnosis of relevant diseases affecting the retinal microcirculation, such as hypertension or diabetes, among others.
Keywords
Computer-aided diagnosis
Vascular structure
Retinal imaging
Optical Coherence Tomography
Vascular structure
Retinal imaging
Optical Coherence Tomography
Description
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11517-017-1660-8
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ISSN
0140-0118