Segmentation-less, automated vascular vectorization robustly extracts neurovascular network statistics from in vivo two-photon images
Authors: Mihelic Samuel A. , Sikora William A. , Hassan Ahmed M. , Williamson Michael R. , Jones Theresa A. , Dunn Andrew K. 
Published: June 2020

Recent advances in two-photon microscopy (2PM) have allowed large scale imaging and analysis of blood vessel networks in living mice. However, extracting a network graph and vector representations for vessels remain bottlenecks in many applications. Vascular vectorization is algorithmically difficult because blood vessels have many shapes and sizes, the samples are often unevenly illuminated, and large image volumes are required to achieve good statistical power. State-of-the-art, three-dimensional, vascular vectorization approaches often require a segmented (binary) image, relying on manual or supervised-machine annotation. Therefore, voxel-by-voxel image segmentation is biased by the human annotator or trainer. Furthermore, segmented images oftentimes require remedial morphological filtering before skeletonization or vectorization. To address these limitations, we present a vectorization method to extract vascular objects directly from unsegmented images without the need for machine learning or training. The Segmentation-Less, Automated, Vascular Vectorization (SLAVV) source code in MATLAB is openly available on GitHub. This novel method uses simple models of vascular anatomy, efficient linear filtering, and low-complexity vector extraction algorithms to remove the image segmentation requirement, replacing it with manual or automated vector classification. SLAVV is demonstrated on three in vivo 2PM image volumes of microvascular networks (capillaries, arterioles and venules) in the mouse cortex. Vectorization performance is proven robust to the choice of plasma- or endothelial-labeled contrast, and processing costs are shown to scale with input image volume. Fully-automated SLAVV performance is evaluated on simulated 2PM images of varying quality all based on the large (1.4×0.9×0.6 mm3and 1.6×108voxel) input image. Vascular statistics of interest (e.g. volume fraction, surface area density) calculated from automatically vectorized images show greater robustness to image quality than those calculated from intensity-thresholded images.

Author summary

Samuel Mihelic is a PhD candidate in the Biomedical Engineering Department at the University of Texas at Austin. He graduated from Oregon State University (Chemical Engineering BS, Mathematics BS). He hosts the GitHub repository for the code used in this article:https://github.com/UTFOIL/Vectorization-Public. His research interests are in-vivo neural microvascular image analysis, anatomy, and plasticity.

William Sikora graduated with a BS in Computational Biomedical Engineering from The University of Texas at Austin in May 2020. He is working with Dr. Yuan Yang and the Laureate Institute for Brain Research as a PhD student of Biomedical Engineering at the University of Oklahoma in Tulsa, researching the highly non-linear world of neural coupling and its link to common neurological pathologies such as stroke.

Ahmed Hassan is a graduate of the University of California, Los Angeles and the University of Texas at Austin with a Bachelor's degree in Microbiology, Immunology, and Molecular Genetics and an MSE/PhD in Biomedical Engineering. His graduate research was concentrated in imaging and instrumentation, and his interests include developing optical and laser systems for neuroimaging, image processing and reconstruction, and advanced image analysis.

Michael Williamson earned a BSc (Honours) in Neuroscience in 2016 from the University of Alberta, where he trained with Dr. Fred Colbourne. He is currently a doctoral student at the University of Texas at Austin working in the labs of Drs. Theresa Jones and Michael Drew.

Theresa Jones is a Professor in the Department of Psychology and Neuroscience at The University of Texas at Austin. Her laboratory studies plasticity of neural structure and synaptic connectivity following brain damage and injury.

Andrew K. Dunn is the Donald J. Douglass Centennial Professor of Engineering in the Department of Biomedical Engineering at The University of Texas at Austin and the Director of the Center for Emerging Imaging Technologies. His research focuses on the development of innovative optical imaging techniques for studying the brain.