## 4 Discussion  
In this study, we proposed a framework to directly estimate the EPVS rating from T2-weighted MRI without using either detection or segmentation of EVPS. The overall accuracy of 87.7% at image level and 80.2% at subject level with the proposed method shows that the EPVS rating can be directly estimated from T2-weighted MRI images. We implemented our method following the same procedure as radiologists do. In clinical practice, radiologists choose the EVPS score based on the highest number of EPVS across multiple slices of MRI. As a result, we implemented this decision process as a majority vote using the results of image-level predictions. Since we used majority voting in the subject level prediction, the performance of the model at subject level was less accurate when compared to its performance at image level. We observed this decrease in performance because the correct prediction at the image-level in the presence of a low EPVS score could not be considered at the subject-level prediction. Individual analyses of each subject's EPVS rating prediction reveals that for incorrectly predicted subjects the prediction score was lower than the manual score in most subjects (15 out of 19). One possible explanation is that the number of subjects presented in the range of [0, 2] is higher than the number of subjects presented in the range of [3, 4]. Another reason may be that many of the EPVS numbers were close to the critical number of the decision boundary and may have caused under-rating. We can see from the result analysis of each group that the predictions of all groups at image level are like the predictions at subject level, except for normal subjects (92.3% at image-level, and 75.7% at subject-level). This discrepancy was caused by the strict criteria of rating at zero level (zero means that there are no EPVS in any of the images corresponding to a particular subject. Additionally, we used majority voting from the six images and predicted the score at the subject level). More sophisticated methods of voting could nullify this gap between subject level and image level. This study has a limitation for comparison with other state-of-the-art methods due to its different labeling scheme. We labeled the EPVS as the score (weak labeling), but for comparison with segmentation networks we must label the EPVS as masks of MRI slices (strong labeling), in which there are 20-50 EVPS (About 50% or more of the total data) in one slice. Moreover, one existing method for EPVS quantification is 3D modeling, but in our case we used a 2D model. We followed [PERSON] et al. 's method [PERSON] et al. (2018) for making the labeling as widely applicable to clinical practice as possible and this means using 2D slice-based measurements of portions of the basal ganglia region in MRI images. The existing 3D models use the whole basal ganglia region of MRI images. Therefore, we simply compared our model with the traditional method as a baseline approach and of the two our model gave significantly better performance. While doing manual ratings of EPVS, we first identified EPVS on T2-weighted MRI images and then confirmed this with T1-weighted MRI and FLAIR scans. However, the study includes only the T2-weighted MRI in the analysis. This is due to the variations in the acquisition parameters such as direction of acquisition, image matrix, FOV, and slice thickness. Though the T1-weighted MRI and FLAIR could provide additional features and improve the accuracy further, it is not convenient to apply the image registration between T1-weighted, T2-weighted, and FLAIR images. Thus, although T2-weighted MRI alone is used to directly predict the rating of EVPS, we obtained comparable accuracy to state-of-the-art methods at both image- and subject-level. Direct grading of the EPVS rather than detection or segmentation is more convenient and efficient in clinical practice.