Mapping the density distribution pattern of microvasculature in whole-mouse brain

Analyzing the vascular network structure of the brain is crucial for understanding brain function and preventing and treating cerebrovascular diseases. By studying indicators such as the length density and branching point density of the brain’s microvascular network, we can better understand the brain’s vascular network and provide a scientific basis for cerebrovascular disease research. Traditional methods rely on statistical analysis of isolated brain regions, which fail to capture the spatial distribution characteristics of micro-vessels across the entire brain and overlook spatial information from different brain regions. To address this, we propose a computational pipeline for the spatial distribution of vascular density in the brain. First, we use a deep learning model to segment brain vascular images from HD-fMOST imaging of mouse brains. Then, we perform skeletonization on the 3D vascular segmentation images and obtain vessel diameter information through distance transformation. Finally, we filter capillaries based on their diameter and calculate the feature parameters of these capillaries to acquire vascular parameter distributions across the entire brain. The entire process is enhanced by MPI parallel processing and multithreading techniques to improve image processing efficiency. Results indicate that our proposed method can capture the spatial distribution characteristics of microvascular density across the whole brain by integrating multiple datasets. This method provides a powerful tool for comprehensive research on brain vasculature.