## 4 Discussion  
The results of the present study show that the three types of data augmentation were useful for the pancreas segmentation in both the baseline U-net and deep U-net. In addition, the deep U-net, which is characterized by additional layers, was overall more effective for automatic pancreas segmentation than the baseline U-net. In data augmentation, not only the conventional method, but also mixup and RICAP were useful for pancreas segmentation; the combination of mixup and RICAP was the most useful. Table 3 summarizes results of previous studies using the Pancreas-CT dataset. While Table 3 includes the studies with coarse-scaled models, Table A1 includes the studies with fine-scaled models. As shown in Table 3, the coarse-scaled 2D model of the current study achieved sufficiently high accuracy, comparable to those of previous studies. While the present study focused on the 2D coarse-scaled models, the data augmentation methods used in the present study can be easily applied to 3D fine-scaled models. Therefore, it can be expected that the combination of the proposed data augmentation methods and 3D fine-scaled models might lead to further improvement of automatic pancreas segmentation. Data augmentation was originally proposed for the classification model, and the effectiveness of mixup was validated for segmentation on brain MRI images [25]. The results of the current study demonstrate the effectiveness of multiple types of data augmentation methods for the two models of U-net for automatic pancreatic segmentation. To the best of our knowledge, the current study is the first to validate the usefulness of multiple types of data augmentation methods in pancreas segmentation. Table 2 shows that deep U-net was better than baseline U-net. Deep U-net included additional layers in its network architecture, compared with baseline U-net. It is speculated that these additional layers could lead to performance improvement for pancreas segmentation. [PERSON] et al. [26] showed that deeper U-net could efficiently denoised low-dose CT images. They also showed that deeper U-net was better than baseline U-net. [PERSON] et al. [4] showed that their U-net with additional layers was effective for uterine segmentation. The results of the current study are consistent with the results of these studies. The effectiveness of deep/deeper U-net has not been sufficiently investigated so far. Because U-net can be used for segmentation, image denoising, detection, and modality conversion, it is necessary to evaluate what tasks the deep/deeper U-net is effective for. Combined use of mixup and RICAP was the best for data augmentation in the current study. The combination of mixup and RICAP was also used in the study of bone segmentation [24]. The results of bone segmentation show that effectiveness of data augmentation was observed in the dataset with limited cases, and the optimal combination was conventional method and RICAP. Based on the studies of bone and pancreas segmentation, usefulness of combination of conventional method, mixup, and RICAP should be further investigated. [PERSON] et al. used CycleGAN as data augmentation to improve generalizability in organ segmentation on CT images [27]. CycleGAN was also used for data augmentation in the classification task [28]. Because the computational cost of training CycleGAN is relatively high, the use of CycleGAN