Abstract
Objective and Impact Statement 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 808×808×702 µm.
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 10× faster computation in terms of voxels-segmented-per-second and 3× 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 deep-learning 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.
Competing Interest Statement
The authors have declared no competing interest.