## 5 Conclusions and Future Works  
This work used different CENs to determine the optimal MRI sequence for the automatic segmentation of MS. The utilization of CENs with different decoding pathways was vital to ensuring that a given sequence would yield the best performances independent of the model that used the sequence as an input. Additionally, this work highlights the U-Net++ with the ResNeXt-50 as a viable deep learning segmentation model that has not previously been used for the automatic segmentation of MS. These data show that models using FLAIR sequences will tend to outperform models using T1 or T2 sequences. These results, in a broader context, support prioritization of FLAIR sequences, which can potentially save costs for patients and doctors. However, since there are many steps that come from diagnosing and segmenting MS, further research is permitted. Regardless, compared to time intensive manual methods, the U-Net++ with the ResNeXt-50 only took 5.7126 s to obtain a DSC of 0.8250. Additionally, when considering multiple different scanners, the U-Net++ with the ResNeXt-50 obtains a relatively high DSC of 0.7159 when using only the FLAIR sequence. However, a limitation of this research with regards to performances had been the low patient-wise precision. Although the U-Net with ResNeXt-50 and U-Net++ with ResNeXt-50 had achieved high DSCs for some the other patients, one of the testing patients from the UML dataset had a relatively poor DSC (0.4095), even when using the U-Net++ with the ResNeXt-50. This is most likely due to the nature of the sample size for the dataset used in that experiment (30 patients). Hence, one of the most pertinent future directions of this research is to uses these models on a bigger dataset. Although properly labeled datasets that include all types of MRI sequences are not always publicly available, there are still data that can be utilized with the goal of exploring the ResNeXt-50 with the U-Net++. It is important to consider that datasets with low inter-rater variability will often be necessary in training because of the discrepancies that appear in ground truth that do not have a viable plan for consensus delineation. These results indicate that if only one MRI contrast image is available or can be considered, it would be favorable for it to be a FLAIR sequence. However, it is important to note that many automatic segmentation pipelines use multiple MRI sequences as the input and have also achieved robust segmentation performances. Thus, a prominent future direction is the utilization of these models to compare FLAIR MRIs between other multi-contrast MRI combinations. As referenced earlier, the utilization of the Tversky loss function should be considered as a means of mitigating class imbalance during future training. Although it is derived from DSC, the Tversky loss function uses parameters that can be fine tuned for a given segmentation model, and it can potentially increase the robustness of current results. With this in mind, future training should also incorporate other known models such as the 3D U-Net and, more recently, the E-Net which has shown to be robust yet computationally efficient. Although the 3D U-Net had not been considered due to concern regarding computational resources, for the purposes of training and testing in the future, it is possible to investigate this by using a Tensor Processing Unit (TPU) to expedite the training process. Regardless, there are still very promising implications of this work based on the performances of these networks. Although future research studies are necessary, an important contribution of this work is that it uses multiple networks and different scanners to further solidify the importance of FLAIR MRIs. In turn, this work demonstrates the ability of a U-Net++ with ResNeXt-50 in obtaining high DSC scores when using these FLAIR MRI sequences as the primary input. Conceptualization, S.G. and M.H. ; methodology, S.G. and M.S.A.S. ; software, S.G.; validation, S.G., M.H. and M.S.A.S.