Arterial cerebral vessel assessment is critical for thediagnosis of patients with cerebrovascular disease e.g., hypertension, Intracranial aneurysms, and dementia. Magnetic resonance angiography is a primary imaging technique for diagnosing cerebrovascular diseases. There are many Convolutional neuralnetworks (CNN) based methods for cerebral vessel segmentation but lack to identify the target vessels and understand the arterial tree structure for diagnosis and endovascular surgical planning.In the present study, we generated annotations for major vesselsegmentation and analyzed fully automatic segmentation of major vessels using state-of-the-art U-Net based deep learning models. Computer-aided major cerebral vessel segmentation incorporatedinto clinical practice may help speed up the diagnosis of time-critical vessel anomalies and help find important bio-markers for neurological dysfunction. We validated and compared U-Net based models for volumetric segmentation and predictionof cerebral arteries and it could be done in real-time withoutany image pre-processing.