Segmentation-Less, Automated, Vascular Vectorization

Recent advances in two-photon fluorescence microscopy (2PM) have allowed large scale imaging and analysis of blood vessel networks in living mice. However, extracting network graphs and vector representations for the dense capillary bed remains a bottleneck 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 vector extraction algorithms to remove the image segmentation requirement, replacing it with manual or automated vector classification. Semi-automated 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 mm3 and 1.6×108 voxel) 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 Remarkably little is known about the plasticity (i.e. adaptability) of microvasculature (i.e. capillary networks) in the adult brain because of the barriers to acquisition and processing of in vivo images. However, this basic concept is central to the field of neuroscience, and its investigation would provide insights to neurovascular conditions such as Alzheimer’s, diabetes, and stroke. Our team of (biomedical, optical, and software) engineers is developing the pipeline to image, map out, and monitor the capillary blood vessels over several weeks to months in a healthy mouse brain. One of the major challenges in this workflow is the process of extracting the capillary network roadmap from the raw volumetric microscope image. This challenge is exacerbated by in vivo imaging constraints (e.g. low/anisotropic resolution, low image quality (noise/artifacts), non-standard (tube-shaped) contrast agent). To confront these issues, we developed a general-purpose software method to extract vascular network maps from low quality images of all sorts, enabling researchers to better quantify the vascular anatomy portrayed in their images. The benefit will be a better understanding of the neurovasculature on the spatial and temporal scales relevant to fundamental cellular processes of the brain.