A robust method is presented for the segmentation of the full cerebral vasculature in 4-dimensional (4D) computed tomography (CT). The method consists of candidate vessel selection, feature extraction, random forest classiffcation and postprocessing. Image features include among others the weighted temporal variance image and parameters, including entropy, of an intensity histogram in a local region at di
erent scales. These histogram parameters revealed to be a strong feature in the detection of vessels regardless of shape and size. The method was trained and tested on a large database of 264 patients with suspicion of acute ischemic stroke who underwent 4D CT in our hospital in the period January 2014 to December 2015. In this database there is a large variety of patients observed in every day clinical practice. The method was trained on 19 4D CT images of patients with manual annotations by two trained medical assistants. Five subvolumes representing different regions of the cerebral vasculature were annotated in each image in the training set. The evaluation of the method was done on 242 patients. One out of fve subvolumes was randomly annotated in 159 patients and was used for quantitative evaluation. Segmentations were inspected visually for the entire study cohort to assess failures. A total of 16 (<8%) patients showed severe under- or over-segmentation and were reported as failures. Quantitative evaluation in comparison to the reference annotation showed a Dice coeffcient of 0.91 +- 0.07 and a modiffed Hausdorff distance of 0.23 +- 0.22 mm, which is smaller than voxel spacing.