## 4 Discussion
Based on the average error rates of volumes per dataset, the error rate of GM, SOL, and sum was 10%. The error rate in the GL volume was large, probably due to the presence of many false positives as in DSC. In addition, the predicted images were overestimated in all muscles, which might be due to the false positives because they were surrounded by other muscles and fat. Of the images used for training, the images with all three muscles present were the most common; however, there were few images at the level of the knee where only the gastrocnemius muscle was present, and the quadriceps muscles were mistakenly detected as gastrocnemius. In addition, images with contrast and roughness were also detected that made it difficult to see the boundaries between the muscles, and false positives were frequent in these images. The interpolation method was evaluated in-plane by calculating the DSC of the supervised and interpolated images. All muscles had mean DSC larger than 0.8. The evaluation of Table 4 was DSC evaluated only with interpolated images; hence, it can be said that the interpolation was performed with high accuracy. In this interpolation method, the centroid of the larger muscle contour was discovered, and the centroid was connected to the dots that make up the contour; the dot with the shortest distance between the line and that of the smaller muscle contour were adopted. If a dot in the false positive area was adopted, the DSC was lowered. Even if they were not considered in this interpolation, all of them were >0.8, so the correct dot with a higher probability among the corresponding dots in the small contour was selected. In a study by [PERSON] et al. [23], the results of an interpolation to measure the intracranial volume were comparable to those of manual segmentation, and its use led to the development of a software for rapid measurement. In a study by [PERSON] et al. [24], to calculate the volume of the quadriceps muscle in MRI images, four different methods were used to calculate the error rate; it was reported that the interpolation method was the best. These results using the interpolation method showed the same trend as these studies. The interpolation method was evaluated along the slice direction by calculating and comparing the error rates of muscle volumes, including the supervised and interpolated images. The volume contained interpolated images generated from the two images but with low error rates. Therefore, the results in Table 5 demonstrate that the interpolation method is effective in reducing the volumetric error. The percentages of GM, SOL, and total errors were >10%; for the total error, the percentage was as low as 7.5% because if there were no false positives in the images before and after the interpolated images, there were no false positives in the interpolated images. This was believed to lead to a decreased error rate because of the possibility of reduced false positives. Even if false positives existed, it seemed that the probability of adopting dots that constitute false positives was low. There was no significant difference between the volumes of the supervised and interpolated images, indicating that this method can be used without any problems in calculating the volume. From the results in Tables 4 and 5, interpolation was performed by excluding the even numbered images from the subsequent evaluation. As these findings underline that the interpolation was accomplished with high accuracy, while observing at the same time the effect of volume reduction, the error with the actual volume could be further reduced by interpolating the actual captured images that were found. This study has some limitations. First, this study was unable to interpolate all images used in the test. Because muscles were present in the supervised images and the even number of those present in the predicted images were excluded, if the muscles were small in the supervised images, they could not be detected and the images could not be complemented.