3DVascNet: an automated software for segmentation and quantification of vascular networks in 3D

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3DVascNet: an automated software for segmentation and quantification of vascular networks in 3D

Authors

Narotamo, H.; Silveira, M.; Franco, C. A.

Abstract

Background: Analysis of vascular networks is an essential step to unravel the mechanisms regulating the physiological and pathological organization of blood vessels. So far, most of the analyses are performed using 2D projections of 3D networks, a strategy that has several obvious shortcomings. For instance, it does not capture the true geometry of the vasculature, and generates artifacts on vessel connectivity. These limitations are accepted in the field because manual analysis of 3D vascular networks is a laborious and complex process that is often prohibitive for large volumes. Methods: To overcome these issues, we developed 3DVascNet, a deep learning (DL) based software for automated segmentation and quantification of 3D retinal vascular networks. 3DVascNet performs segmentation based on a DL model, and it quantifies vascular morphometric parameters such as the vessel density, branch length, vessel radius, and branching point density. Results: We tested 3DVascNet\'s performance using a large dataset of 3D microscopy images of mouse retinal blood vessels. We demonstrated that 3DVascNet efficiently segments vascular networks in 3D, and that vascular morphometric parameters capture phenotypes detected by using manual segmentation and quantification in 2D. In addition, we showed that, despite being trained on retinal images, 3DVascNet has high generalization capability and successfully segments images originating from other datasets and organs. Moreover, the source code of 3DVascNet is publicly available, thus it can be easily extended for the analysis of other 3D vascular networks by other users. Conclusions: Overall, we present 3DVascNet, a freely-available software that includes a user-friendly graphical interface for researchers with no programming experience, which will greatly facilitate the ability to study vascular networks in 3D in health and disease. Keywords: Vasculature Segmentation, Morphometric Analysis, 3D Microscopy Images, Deep Learning, Automated Software

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