Background and Purpose: Thrombus characterization is increasingly considered important in predicting treatment success for patients with acute ischemic stroke. The lack of intensity contrast between thrombus and surrounding tissue in CT images makes manual delineation a difficult and time consuming task. Our aim was to develop an automated method for thrombus measurement on CT angiography and validate it against manual delineation. Materials and Methods: Automated thrombus segmentation was achieved using image intensity and a vascular shape prior derived from the segmentation of the contralateral artery. In 53 patients with acute ischemic stroke due to proximal intracranial arterial occlusion, automated length and volume measurements were performed. Accuracy was assessed by comparison with inter-observer variation of manual delineations using intraclass correlation coefficients and Bland-Altman analyses. Results: The automated method successfully segmented the thrombus for all 53 patients. The intraclass correlation of automated and manual length and volume measurements were 0.89 and 0.84. Bland-Altman analyses yielded a bias (limits of agreement) of -0.4 (-8.8, 7.7) mm and 8 (-126, 141) mm(3) for length and volume, respectively. This was comparable to the best interobserver agreement, with an intraclass correlation coefficients of 0.90 and 0.85 and a bias (limits of agreement) of -0.1 (-11.2, 10.9) mm and -17 (-216, 185) mm(3). Conclusions: The method facilitates automated thrombus segmentation for accurate length and volume measurements, is relatively fast and requires minimal user input, while being insensitive to high hematocrit levels and vascular calcifications. Furthermore, it has the potential to assess thrombus characteristics of low-density thrombi.