## 4 Results and Discussion
Hence, the differences in performances can come from discrepancies in upsampling features or the abundance of skip connections. However, skip connections are mainly used for managing gradients during back propagation; thus, it could be dependent on T1 sequences specifically obtained from the scanner used in the UMCL dataset. Regardless, these data show that the FLAIR MRI sequence has a tendency to yield better performances when used for segmentation. Additionally, when using the FLAIR sequence, the U-Net++ CEN and the U-Net CEN had a marginal difference with respect to DSC (0.0001); this suggests that there is no appreciable difference between these models and that both of them can provide robust segmentation when using the FLAIR MRI sequence. Since each of the four models tested on six patients with four different MRI sequences, there were a total of 96 different performances. Table 4 is a contingency table, which demonstrates how often a model using FLAIR had achieved the best performance (Rank 1) and how often a model using the other inputs did not achieve the best performance. A [PERSON]'s exact test took the distribution of the performances into account, and it had indicated that these results had been statistically significant. This validates the observed results for the FLAIR MRI sequence during testing and training. Although more performances can be used to further validate these results, based on these data, it holds that the FLAIR MRI sequence is the optimal MRI sequence for automatic segmentation. Figure 5: U-Net++ with ResNeXt-50 using the T1 sequence during training. Shown are the DSC, IoU, and loss per epoch during training. Table 5 displays the time it took to segment each patient's brain and the corresponding DSC for the U-Net and U-Net++ CENs using FLAIR. It is important to note that although the U-Net with ResNeXt-50 had the best average DSC (0.6678), the U-Net++ with ResNeXt-50 had the best case DSC (0.8250) from patient six. However, it is also important to note the high variability among patients, specifically the outlier of patient four. Fortunately, this could be significantly improved with the availability of more patient data. Regardless, both models were capable of achieving very robust segmentations in the range of seconds when using only the FLAIR MRI. A possible explanation for why the FLAIR MRI sequence yielded the best performance for each of the four models is most likely due to the way the lesions are numerically represented. Since CENs classify based on the number associated with a voxel (higher numbers indicate brighter lesions), they are ultimately classifying based on brightness. Hence, the MRI sequence that depicts lesions the brightest would intuitively yield better performances. However, although FLAIR lesions are darker compared to T2 lesions, FLAIR lesions are surrounded by dark brain matter. This facilitates a CEN in identifying bright lesion voxels among dark background voxels. Additionally, since the T2 sequence often depicts CSF as brightly as hyperintense white matter lesions, it is likely that a given model would have a difficult time differentiating between healthy and diseased brain matter. Although the initial reasoning for using T2 sequences clinically was that lesions could be better identified in the infraentorial sections of the brain [42], these data suggest that the ambiguity of determining a lesion from T2 sequences ultimately give way to better performances with the use of FLAIR sequences in the context of automatic segmentation. Figure 6 shows the U-Net++ CEN predictions of lesion locations during testing. It is important to note that while larger volumes of lesions are able to be segmented proficiently by the model, there are still differences between the predictions and ground truth for smaller lesion fragments. This is most likely due to the difficulty in classifying small lesion volumes in isolation.