Segmentation-less, automated vascular vectorization robustly extracts neurovascular network statistics from in vivo two-photon images

Abstract

Recent advances in two-photon microscopy (2PM) have allowed large scale imaging and analysis of cortical 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 require a segmented/binary image, relying on manual or supervised-machine annotation. Therefore, voxel-by-voxel image segmentation is biased by the human annotator/trainer. Furthermore, segmented images oftentimes require remedial morphological filtering before skeletonization/vectorization. To address these limitations, we propose a vectorization method to extract vascular objects directly from unsegmented images. 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 lowcomplexity 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 microvascu- lar networks (capillaries, arterioles and venules) in the mouse cortex. Vectorization performance is proven robust to the choice of plasma-or endotheliallabeled contrast, and processing costs are shown to scale with input image volume. Fully-automated SLAVV performance is evaluated on various, simulated 2PM images based on the large, [1.4, 0.9, 0.6] mm input image, and performance metrics show greater robustness to image quality than an intensity-based thresholding approach.

Mathematical synthesis of the cortical circulation for the whole mouse brain—part II: Microcirculatory closure

“…Moreover, these anatomically sound circulatory networks can serve as a ground truth or a phantom model for validating homogenization techniques 7,20 or reconstruction techniques. 3,24,25,41 Homogenization can be a useful tool for simplifying massive microvascular computations, but its validation can benefit from comparison with a more complete model presented here to ensure that critical trends are not lost by diverse homogenization assumptions.…”

“…The Boas ( n = 5) and Dunn ( n = 6) cohorts applied in vivo imaging with open cranial windows. Based on imaging data, vascular network graphs (VANs) were reconstructed as described previously 3,24,25 …”

“…Based on imaging data, vascular network graphs (VANs) were reconstructed as described previously. 3,24,25 Segmentation-independent morphometric statistics were computed using cubic Bezier spline segments for all murine image-derived vascular networks as described in Appendix B. We calculated probability density functions (PDFs) and normalized cumulative density functions (CDFs) for length, diameter, surface area, volume, and tortuosity spectra.…”

“…Image segmentation of multiphoton image data produces cylindrical segments of arbitrary length depending on image thresholds and algorithms used (see 4,24,25,46,47 ). Unfortunately, this means that segment count and spatial partitioning in reconstructed networks are not standardized, meaning the raw segment count is not a suitable basis for morphometric analysis.…”