Two-photon microscopy (TPM) can provide a detailed microscopic information of cerebrovascular structures. Extracting anatomical vascular models from TPM angiograms remains a tedious task due to image degeneration associated with TPM acquisitions and the complexity of microvascular networks. Here, we propose a fully automated pipeline capable of providing useful anatomical models of vascular structures captured with TPM. In the proposed method, we segment blood vessels using a fully convolutional neural network and employ the resulting binary labels to create an initial geometric graph enclosed within vessels boundaries. The initial geometry is then decimated and refined to form graphed curve skeletons that can retain both the vascular shape and its topology. We validate the proposed method on 3D realistic TPM angiographies and compare our results with that obtained through manual annotations.