## 5 Discussion
### Comparison with Similar Studies on Image Segmentation  
While image segmentation on phase contrast scans of the neck has not been broadly investigated in patients with cerebrovascular diseases, deep learning has been employed to perform similar tasks in lesion segmentation in patients with glioma or stroke. For instance, [PERSON] et al. applied the DenseNet121 based U-Net framework to identify and stage glioma using MR FLAIR data. A DSC score of 0.93 was achieved in a cohort consisting of 208 patients [20]. In terms of image segmentation for stroke patients, [PERSON] et al. demonstrated that an efficient implementation of the Standard U-NET can achieve a DSC of 0.77 for segmenting stroke lesions in T1-weighted MRI scans [21].  
The use of deep learning to segment objects of interest in MRI images is now a well-known approach. In this study, we focused on Standard U-Net and its variations, such as Attention and Nested (UNet++). However, in the field of medical imaging segmentation, there are several other state-of-the-art models used. For instance, the SegNet approach is an encoder-decoder network that produces class probabilities for each pixel. The encoder convolution produces feature maps using filter banks which are batch normalized and adjusted using ReLU = max (0, x). The 2 \(\times\) 2 max-pooling and non-overlapping window results in the output being subsampled by a factor of 2. Each decoder corresponds to the 13 encoder layers and the final decoder output is sent to a soft-max classifier which independently classifies each pixel. Due to the use of pooling indices, the model works efficiently using low memory, but can be used for large images with high accuracy. While traditionally used for landscape segmentation, it was recently utilized successfully in medical imaging. Another study found that SegNet was better able to classify infected and non-infected lung tissue [22]. Its ability to segment landscapes was beneficial in distinguishing large features in normal and abnormal tissue. However, the loss of information during unpooling results in low resolution when compared to UNet models that use the entire feature map. The low resolution of this model would prevent it from providing accurate segmentation of small arteries and paired cerebral blood flow data. The ResUNet++ is an improvement based on the Deep Residual U-Net architecture, which combines U-Net and deep residual learning [23]. The ResUNet++ model utilizes the attention block, residual blocks, Atrous Spatial Pyramid Pooling (ASPP), and the squeeze and excitation block. The addition of deep residual learning decreases the reuse of features between blocks, therefore allowingdeeper neural networks that could resolve issues with degradation. The residual blocks combine the convolutional layers, batch normalization, and linear rectification (ReLu). Typically, there are three encoder layers with three corresponding decoder layers, as well as ASPP. The attention block helps generate more effective feature maps by analyzing which positions require more attention before the encoder units and ASPP allows for broader context after the encoder outputs are filtered. The output of the decoder is also fed to the ASPP, which results in a \(1\times 1\) convolution. The deep neural network of the model reduces percent error and allows higher image classification accuracy. ResUNet++ has been used to successfully segment lungs in chest X-ray images with a small data set and high accuracy, as well as distinguish different cardiovascular structures [24; 25]. Compared to the UNet models used in our study, ResUNet++ has similar validation accuracy, but requires a much longer training time and more memory.