## 4 Discussion  
In this study, automatic segmentation of surface muscles by a 3D U-Net was performed using training VPs composed of 56 slices selected at equal intervals in each case from whole-body CT images for the training algorithm. First, we compared the results of this study with a conventional method [13]. In the conventional method [13], surface muscle segmentation was performed in a 2D U-Net using all slices of the whole body, with an average DC of 0.749. The average DC was 0.900 using the VP composed of 56 slices, and a high segmentation rate was obtained with the proposed method. In the VP-based method,  
Figure 3: An example of the automatic segmentation result of the surface muscle for Case E and comparison with our 2D U-Net based approach [13], when the dice coefficient was 0.937 in the second experiment. Yellow, red, and green show the overlapped area between ground truth and the result, the over-extracted and unextracted regions, respectively, for the (**a**) coronal, (**b**) sagittal planes, and (**c**) axial planes. the 3D U-Net could extract relative surface muscle features between slices even with a limited number of training slices. The point of this study was to show that for algorithm training, 56 images selected at equal intervals, in each case, from the whole-body CT images obtained a higher average DC value without using all slices. Despite the random selection of training and validation data in three experiments,  
Figure 4: Slice example of the segmentation results in each section [DC (dice coefficient) in case, DC in slice]. The left column is a good result and the right column is a bad result. Yellow, red, and green show the overlapped area between ground truth and the result, the over-extracted, and unextracted regions, respectively. In **(a,e)**, the segmentation results were accurate for areas not adjacent to the limbs and abdominal organs. Over-extraction occurred in the mammary gland area (**b**) and the area where the surface muscle was adjacent to the abdominal organ (**c**). Un-extraction occurred in the surface muscles of the abdomen where the recognition target was small (**d**). DC, dice coefficient. the mean DC was 0.900 and (SD: 0.022). This can be considered to be a very stable result. In this study, 56 slices were selected from each case at equal intervals for the VP creation. Furthermore, the number of slices and the imaging range of test images are different in each case. Therefore, by selecting slices at equal intervals, we considered that each VP is composed of surface muscles at various anatomical positions, trained with many surface muscle variations, and obtained high generalization performance for the test VP. In other words, since the number of slices in the image database is 1305-1726, when 56 slices are selected from each case, it can be said that surface muscle segmentation of the whole body is performed with the limited selection of slices of (3.2-4.3% of the whole). In this way, the method of selecting slices at equal intervals during VP construction is also an effective method for the generation of annotation images in the automatic segmentation of surface muscles. There are some limitations to our study. First, the number of images when creating a VP was fixed at 56. For the creation of the VP, we selected 56 because it was the maximum number of slices that could be learned by the GPU in this experimental environment. Thus, we did not consider another number of slices. Therefore, for the segmentation of the surface muscles using VP, determination of the number of slices composing the VP remains to be studied. However, it is necessary to consider that changing the number of slices requires the creation of new ground truth labels. In this study, surface muscles were defined as skeletal muscles outside of the body cavity, and whole-body CT images taken with a standard protocol were used. The training increases as the number of slices increases, so improving the DC rate is possible.