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.

中文翻译：

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广义深度学习在双光子显微镜下脑血管的解剖模型

从大脑的两光子显微镜（2PM）血管造影术中进行血管分割在血液动力学分析和疾病诊断中具有重要的应用。在这里，我们开发了一种通用的深度学习技术，用于从多个2PM设置中获取的小鼠大脑中相当大的区域的准确2PM血管分割。此外，该技术在计算上非常有效，使其非常适合大规模神经血管分析。简介：从2PM血管造影术中进行血管分割通常是脑血管系统血流动力学建模的重要第一步。现有的基于深度学习的最新分割方法要么缺乏对来自各种成像系统的数据进行概括的能力，要么对于大规模血管造影在计算上是不可行的。在这项工作中 我们提出了一种方法，可通过将其推广到各种成像系统来改善这些局限性，并且还能够分割非常大的血管造影照片。方法：我们采用基于半监督学习策略的计算有效的深度学习框架，该方法的有效性在实验获得的，尺寸高达808x808x702微米的小鼠大脑的体内血管造影照片上得到了证明。结果：仅对一台2PM显微镜的数据进行训练后，我们对另一台显微镜的数据进行了血管分割，而无需进行任何网络调整。与最新技术相比，我们的方法论证了以每秒细分的体素速度快10倍，深度提高了3倍。结论：我们的工作为大脑脉管系统提供了一个可通用且计算效率高的解剖模型框架，该框架包括基于深度学习的血管分割，然后进行图形绘制。它为将来无法进行的更大范围的血液动力学反应建模和分析铺平了道路。