## 3 Methodology
### Models Used  
As stated earlier, four different CENs were used: ResNeXt-50 with FPN, ResNeXt-50 with Linknet, ResNeXt-50 with U-Net, and ResNeXt-50 with U-Net++. The U-Net model was proposed for biomedical image segmentation [10], and it is among some of the current state-of-the art methods. It functions by the same logic of basic encoder-decoder pathways, except with this model the decoding pathway consists of unique up-convolution layers to scale the feature maps to a readable output segmentation. The U-Net++ was proposed after the introduction of the U-Net model [27]. It was created to improve upon the U-Net's segmentation performance by increasing the number of skip connections. By using more intermediate skip connections, the U-Net++ can extract finer details in features that may not be accounted for in the normal U-Net; this is very important in the context of biomedical imaging since fine details often correspond to significant biomarkers [27; 28]. The Feature Pyramid Network (FPN) is a network architecture that uses features in a different manner than the aforementioned models [29]. For FPNs, the model generates a series feature maps with decreasing size and stacks them in a pyramid shape. These feature maps are then converted into actual predictions using an activation function. These predictions are compiled into a final segmentation output image by using the sums of each tier within the feature pyramid. The Linknet model is considered to be a relatively fast and efficient model for segmentation [30]. Generally speaking, it is also the lightest out of these four in terms of the actual architecture. Although this model does not have as many layers as the aforementioned models, previous studies have shown that for other segmentation tasks, excluding biomedical segmentation including MS, the model has been able to yield relatively good performances [31]. Encoder  
Each of these models had the same ResNeXt-50 encoder [32] backbone trained on ImageNet [33] weights in order to allow for faster training. This also eliminates the  
Figure 2: Manual segmentation pipeline from UMCL dataset [25]. chance that discrepancies within performances could be attributed to differences in feature extraction. By controlling for the encoder, performances are dependent on how a model used extracted features. Thus, the optimal MRI sequence would yield the best performance depending on how a model upsamples extracted features. It is important to clarify that the ResNeXt-50 encoder consists of 6 scales (original, 1/2, 1/4, 1/8, 1/16, and 1/32), while the original U-Net decoding pathway consists of 5 scales (original, 1/2, 1/4, 1/8, and 1/16). For this reason, the decoding pathway of the U-Net had an additional scale (1/32) to account for the skip connections for each of the ResNeXt-50 feature maps except for the original (Figure 3). These scales had applied to the U-Net++ as well; thus, there were more intermediate skip connections appended to account for the extra scales. The ResNeXt-50 encoding pathway uses a 7 \(\times\) 7 convolutional layer with a stride of 2 to create the first feature map. This is why the original input image is not used for a skip connection; however, this would be an interesting avenue for future research if one were to include the image of original scale in a skip connection for this specific model. Afterwards, each encoder step had used residual blocks, which had consisted of a 1 \(\times\) 1 convolutional layer, a 3 \(\times\) 3 convolutional layer, and a 1 \(\times\) 1 convolutional layer [32]. In Figure 3, each decoding step on the right hand is performed by two 3 \(\times\) 3 convolutional layers and ReLU activation, which compose the decoding pathway of the original U-Net model. These convolutional steps were also present in the U-Net++ decoding pathway, but the inclusion of the 1/32 scale allowed for the creation of additional sets of skip connections.