## 4 Discussion
Therefore, the solution is to improve the detection capability by training more images or to prepare a test image where the number of interpolatable images exists. Second, the error rate of the volume calculated in this study could not be calculated in the legs without gastrocnemius or SOL muscles. Therefore, to get a more correct error rate, it would be necessary to have all the three muscle types in the legs used for training or testing. Third, most studies on quadriceps segmentation used U-Net [16; 17; 18]. In these studies [25; 26], network models were compared for segmentation, and deeplabv3 puls was shown to have the highest detection ability. Therefore, DeepLabv3+ was used for segmentation instead of U-Net because Deeplabv3+, which has a higher detection capability, was more likely to be able to detect muscles with higher accuracy than U-net. Using this algorithm, the triceps could be detected immediately and determine its volume. However, the recent advances in the channel and spatial attention mechanisms [27; 28; 29] have definitely led to the evolution of semantic segmentation using U-Net. Therefore, a further study on CNNs in the state of the art is needed. Fourth, the hyperparameters were not adjusted because this study did not aim to tune the hyperparameters as in the study by [PERSON] et al. [30]. Setting and training the optimal hyperparameters may lead to more accurate detection with Bayesian Optimization. Fifth, the sample size of data is small. Not only is it difficult to guarantee generalization performance with small sample size, but it is also possible to improve accuracy by increasing sample size. Additionally, in this study, training and test images were divided 10:2 for six-fold cross validation, but five- or ten-fold is general [31; 32]. This value was obtained by dividing the data by person, and it is thought that cross-validation can be performed with general values by increasing the sample size. Sixth, there are no studies of triceps surae muscle segmentation using Deep Learning. Therefore, this study was compared with a study of quadriceps segmentation [15; 16; 17; 18; 19; 20] and a study of triceps surae muscle segmentation that did not use deep learning [21]. The results showed that the DSC of the quadriceps was higher than the DSC of this study, which suggests that this study has room for DSC improvement. In addition, it was more accurate than the reported segmentation of the triceps surae muscle. This interpolation method can be used to calculate their volume without any problems found. Although muscle perimeter length and cross-sectional area, which are correlated with muscle volume [33; 34], are sometimes used as indices, it is more accurate to calculate volume from images of the entire muscle [35]. However, it is more accurate to calculate the volume from the whole muscle image. Therefore, this study, which can calculate the actual volume by immediately calculating the volume and interpolating between slices, suggests the possibility of advancing research in the field of physical therapy. The technique of detecting the triceps and calculating their volume can be used to other parts and organs, and it was assumed that the interpolation method can be used to determine the volume more accurately. The interpolation technique may also be useful in smoothing out the images captured with a thicker slice for multiplanar reconstruction processing and 3D visualization.