This paper showcases a u-net based architecture for the automated segmentation images of meningeal lymphatic vessels. These lymphatic vessels surround the cerebral cortex and have been recently found to drain waste from the brain. Studies on mice have shown loss of memory and impairment in cognitive ability if the vessels' draining capacity does not function adequately. As the meningeal lymphatic vasculature itself is a recent discovery, there is no software tailored for automatically segmenting these images. Instead, segmentation must be performed by hand, which is a tedious and errorprone process. By building an automatic segmentation tool for these vessels, we can provide informatics for understanding and researching them, in a quick and reliable way. A convolutional neural network, called u-net, is adapted to the vessel segmentation application, with the goal of teaching the network how to segment the vessels. Segmentation using u-net is compared to traditional non-learning based segmentation methods using Dice coefficient. Three complexity measures are also proposed to evaluate the segmentation quality: vessel ramification index, porosity, and vessel length. The existence of a technique and associated software to automatically segment and analyze these vessels will drastically speed up subsequent neuroscience research in the field.