## 4 Discussion  
With regards to the evaluation of the created models, DSCs were used to compare the agreement of the images and evaluate whether the detection was correct in the two dimensions. Based on the results of the evaluation of the created models, MG and SOL were detected with higher accuracy than GL, with DSCs exceeding 0.8. As for MG and SOL, the DSC of segmentation in the thigh muscle, which exceeded 0.8 in many previous studies, were found to be lower than the respective values in DSCs, but the values themselves cannot be compared because the target areas were different. However, this study could detect MGs with a higher accuracy than the reported segmentation of MGs without using deep learning [22]. For GM and SOL, there were many large muscles present, but there were some false positives, which were detected with a DSC of >0.8 on average. As for the DSCs of GL, GL was shorter in the craniocaudal direction than the other two muscles, and the number of slices in which GL exists is small. The slices at the edge of the GLs were so small that it was difficult for the human eye to recognize them, and the ratio of the number of small GLs slices was higher than that of other muscles. In small muscles where the other muscles and surrounding fat tissues are mistakenly detected as GL, false positives were more common and this was believed to be the cause of the decline in DSCs. Figure 7 is a representative example of partially incorrectly predicted images. Not only are there false detections of the SOL as the GL and GM, but there are also examples of inaccurate location. As per [PERSON]'s study [17], when false positives, which were not present in the surveillance images, appeared in the prediction images, DSC was zero and affected the average DSC. In addition, some images used in the test were blurry, and false positives were particularly common in these. The triceps surae is a long muscle that exists in a craniocaudal direction, and the shape of the muscle changes, particularly at the level of the knee and calf. In this study, the number of slices was uneven in each position; therefore, the number of slices at the knee level was small, which affected DSCs. As there were no reports on the segmentation of the triceps surae, the results were compared with those of the quadriceps. In a study by [PERSON] et al. [16], using T1-weighted 250 images of thighs, including the quadriceps, hamstrings, sutures, adductor muscles, and others, were prepared and trained with U-Net, which reported a muscle-only DSC of >0.9. However, as this was only a result for a specific anatomical location, it may not be suitable for the volume calculation and interpolation that were performed in this study. In a report by [PERSON] et al. [17], 46 thigh \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Comparison Target** & **Position** & **Error Rate [\%]** & \(p\) **Value** \\ \hline \multirow{4}{*}{Supervised muscle and interpolated muscle} & GM & 9.41 \(\pm\) 7.65 & 0.5052 \\  & GL & 17.89 \(\pm\) 6.37 & 0.3098 \\  & SOL & 9.43 \(\pm\) 5.07 & 0.7280 \\  & total error & 7.69 \(\pm\) 3.49 & - \\ \hline \multirow{4}{*}{Supervised muscle and non-interpolated muscle} & GM & 20.28 \(\pm\) 9.68 & 0.1499 \\  & GL & 33.93 \(\pm\) 7.54 & 0.2539 \\  & SOL & 12.70 \(\pm\) 5.79 & 0.3442 \\  & total error & 14.97 \(\pm\) 3.98 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The error rate of volume for the interpolated images. 4968-slice Fat-water decomposition MRI images were trained with U-Net. The mean DSC of the quadriceps, hamstrings, sutures, and thin muscles was 0.89, and the mean percent difference in volume was 7.57%. A similar study was conducted to determine DSC and error rates for different image types and regions used; the results showed a similar trend to the present study, indicating the validity of the present results. With regards to volumetry without interpolation, the agreement was represented in three dimensions by calculating the error rates of the volume.