Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.09.243394v1?rss=1 Authors: Tahir, W., Kura, S., Zhu, J., Cheng, X., Damseh, R., Tadesse, F., Seibel, A., Lee, B. S., Lesage, F., Sakadzic, S., Boas, D. A., Tian, L. Abstract: Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here we develop a generalizable deep-learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. In addition, the technique is computationally efficient, making it ideal for large-scale neurovascular analysis. Introduction: Vascular segmentation from 2PM angiograms is usually an important first step in hemodynamic modeling of brain vasculature. Existing state-of-the-art segmentation methods based on deep learning either lack the ability to generalize to data from various imaging systems, or are computationally infeasible for large-scale angiograms. In this work, we present a method which improves upon both these limitations by being generalizable to various imaging systems, and also being able to segment very large-scale angiograms. Methods: We employ a computationally efficient deep learning framework based on a semi-supervised learning strategy, whose effectiveness we demonstrate on experimentally acquired in-vivo angiograms from mouse brains of dimensions up to 808x808x702 micrometers. Results: After training on data from only one 2PM microscope, we perform vascular segmentation on data from another microscope without any network tuning. Our method demonstrates 10x faster computation in terms of voxels-segmented-persecond and 3x larger depth compared to the state-of-the-art. Conclusion: Our work provides a generalizable and computationally efficient anatomical modeling framework for the brain vasculature, which consists of deeplearning based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before. Copy rights belong to original authors. Visit the link for more info