Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to quantify and analyze brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a convolutional neural network (CNN) with a transfer learning approach for segmentation and achieves human-level accuracy. By using VesSAP, we analyzed the vascular features of whole C57BL/6J, CD1 and BALB/c mouse brains at the micrometer scale after registering them to the Allen mouse brain atlas. We report evidence of secondary intracranial collateral vascularization in CD1 mice and find reduced vascularization of the brainstem in comparison to the cerebrum. Thus, VesSAP enables unbiased and scalable quantifications of the angioarchitecture of cleared mouse brains and yields biological insights into the vascular function of the brain.

组织清除方法可以对生物样本进行成像而无需切片。然而，在三个维度上对大型成像数据集进行可靠且可扩展的分析仍然是一个挑战。在这里，我们开发了一个基于深度学习的框架来量化和分析脑血管，称为血管分割和分析管道（VesSAP）。我们的管道使用带转移学习方法的卷积神经网络（CNN）进行分割，并达到了人类水平的准确性。通过使用VesSAP，我们将整个C57BL / 6J，CD1和BALB / c小鼠脑注册到Allen小鼠脑图集后，以微米尺度分析了它们的血管特征。我们报告CD1小鼠继发性颅内侧血管生成的证据，并发现与大脑相比脑干的血管生成减少。从而，