## 5 Conclusions  
We have described an algorithm to update the GAN generator using the newly proposed dissimilarity coefficient, used along with DSC. The aim is to update the model to not only focus on the true positive area but also on the false positive and false negative area, which will improve the performance. Our method comprises two updates, the first one is based on the true positive, and the second one makes the correction based on the false positive and false negative area. We tested our model with the ideal condition which follows the traditional weight update. From the comparison we found that our model outperformed the others. Also, we have compared our method with the previous methods used for brain tissue segmentation. Again, in this comparison our method outperformed previous approaches. We have highlighted the limitations of a DSC loss function and introduced the dissimilarity coefficient as a new loss function. However, there are a few drawbacks of our method as it is time-consuming and resource-consuming. Despite this, in terms of the DSC scores, our model is the best one. By using segmentation in the different planes for different subjects, we have confirmed that using the dissimilarity coefficient and the alternative update of the generator is, by a considerable margin, more robust and accurate than DSC only. We evaluated and demonstrated the better performance of our model with DSC scores and the VOE scores. In the future work, we will try to study and overcome the drawbacks of our model and try to improve it to be the best in all planes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**DSC Scores**} \\ \cline{3-5}  & & **WM** & **GM** & **CSF** \\ \hline [PERSON] et al. [8] & CNN & 86.4\% & 85.2\% & 83.5\% \\ [PERSON] et al. [9] & FCN & 88.7\% & 87.3\% & 85.5\% \\ [PERSON] et al. [34] & Path-wise UNet & 94.33\% & 93.33\% & 92.67\% \\ [PERSON] et al. [35] & Patch-wise Mnet & 95.17\% & 94.32\% & 93.60\% \\ Proposed method & 96.41\% & 96.23\% & 94.37\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of segmentation results of different approaches. **Author Contributions:** [PERSON] has developed the concept and handled the analysis. The concept has been examined by [PERSON]-R.K., and the findings have been confirmed. The paper was reviewed and contributed to by all authors, and the final version was approved by them all. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1 IIA3050703). And this research was supported by the BrainKorea21 Four Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (429990114316). Additionally, this work was supported by a project for Industry-University-Research Institute platform cooperation R&D funded Korea Ministry of SMEs and Startups in 2022 (S3312710). **Institutional Review Board Statement:** The studies involving human participants were reviewed and approved by IRB No. is 2-1041055-AB-N-01-2019-45/2020-72 in Chosun University. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The datasets utilized in this article were obtained from the OASIS webpage, which is freely accessible for all scientists and investigators to conduct experiments and can be simply accessed from OASIS's website: [[https://www.oasis-brains.org/#data](https://www.oasis-brains.org/#data)]([https://www.oasis-brains.org/#data](https://www.oasis-brains.org/#data)) (accessed on 15 March 2022). **Acknowledgments:** Data were provided by OASIS: Cross-Sectional MRI: Principal Investigators: [PERSON], [PERSON], [PERSON] [PERSON], [PERSON], and [PERSON]; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382. Correspondence should be addressed to [PERSON], [EMAIL_ADDRESS].