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

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 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 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.