## 1 Introduction  
Sufficient blood supply in the brain is important to maintain a healthy cerebral blood flow (CBF) and cerebral autoregulation [1]. Impaired CBF caused by vasculopathy can affect cerebral autoregulation and increase the risk for cerebrovascular diseases and neurological disorders [2]. Moyamoya disease is a progressive cerebrovascular disorder that disturbs normal CBF; patients with Moyamoya have a higher risk for acute and recurrent strokes [3]. Since the brain receives its blood supply directly from the internal carotid and vertebral arteries, measuring the blood flow within these arteries can directly reflect CBF and autoregulation [4]. Several medical imaging modalities have been developed to measure blood flow volume in arteries and arterioles. For instance, phase contrast (PC) MRI is an imaging technique that is commonly used to characterize blood flow velocity without the need for radiation and contrast agents [5]. The measured blood flow velocity may lead to the computation of total flow volume. Several studies have demonstrated the effectiveness of PC MRI in measuring blood flow in patients with arteriopathy [6]. However, its clinical application has been limited by the need for manual segmentation of multiple vessels of interest. This not only deteriorates the reproducibility of the measurements using PC MRI, but also affects quantitative measurements of blood flow. Although commercial software is available for the segmentation of PC MRI, the procedures also require adjustment of operators and thus have many of the same problems as the fully manual method.  
Deep learning is a class of machine learning that has shown great promise in several medical imaging applications [7]. Among the advanced deep learning techniques, deep convolutional neural networks (CNN) have been applied in segmentation and lesion detection tasks in medical imaging [8]. Using acquired data as the input and desired data as the output, a model is trained to determine the optimal parameters for the transformation between input and output. The difference between the output and ground truth is used to adjust the parameters of the model to improve the prediction iteratively. Once trained (or the parameter values determined), the model can be applied to predict the output associated with a new input. For example, an encoder-decoder framework has been applied in segmenting PC MRI of cardiac images to enable fully automated aortic flow quantification, leading to markedly improved efficiency and reproducibility of the segmentation task [9]. In the realm of applications in cervical cancer, several previous studies demonstrated that deep learning methods were developed to identify cervical cancer to improve diagnostic accuracy [10; 11; 12]. Specifically, models developed using MR data showed the potential of a universal model for cervical cancer identification and staging [13; 14; 15]. Applying a CNN to segment arteries in cervical PC MRI is challenging due to the number of arteries of interest (including right and left carotid and vertebral arteries) and the relatively low signal-to-noise ratio of the native images.  
In this work, we present a CNN-based deep learning approach for segmenting carotid and vertebral arteries in PC MRI data from healthy control (HC) subjects and Moyamoya patients. Segmentations created by three different implementations were compared using the Dice Similarity Coefficient (DSC) for overlap and predicted bulk flow volume accuracy compared with manual segmentation.