Anatomical modeling of brain vasculature in two-photon microscopy by generalizable deep learning
Blaire S. Lee,
David A. Boas,
Posted 10 Aug 2020
bioRxiv DOI: 10.1101/2020.08.09.243394
Posted 10 Aug 2020
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. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Vascular segmentation from 2PM angiograms is an important first-step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep-learning either lack the ability to generalize to different imaging systems, or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems, and is able to segment large-scale angiograms. We employ a computationally efficient deep-learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and a total-variation regularization on the network's output. Its effectiveness is demonstrated on experimentally acquired in-vivo angiograms from mouse brains of dimensions up to 808x808x702 micrometers. To demonstrate the superior generalizability of our framework, we train on data from only one 2PM-microscope, and demonstrate high-quality segmentation on data from a different microscope without any network-tuning. Overall, our method demonstrates 10x faster computation in terms of voxels-segmented-per-second and 3x larger depth compared to the state-of-the-art. Our work provides a generalizable and computationally efficient anatomical modeling framework for 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.
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