## 1 Introduction  
Identification of anatomical structures is a fundamental step for radiologists in the interpretation of medical images. Similarly, automatic and accurate organ identification or segmentation is important for medical image analysis, computer-aided detection, and computer-aided diagnosis. To date, many studies have worked on automatic and accurate segmentation of organs, including lung, liver, pancreas, uterus, and muscle [1; 2; 3; 4; 5]. An estimated 606,880 Americans were predicted to die from cancer in 2019, in which 45,750 deaths would be due to pancreatic cancer [6]. Among all major types of cancers, the five-year relative survival rate of pancreatic cancer was the lowest (9%). One of the reasons for this low survival rate is the difficulty in the detection of pancreatic cancer in its early stages, because the organ is located in the retroperitoneal space and is in close proximity to other organs. A lack of symptoms is another reason for the difficulty of its early detection. Therefore, computer-aided detection and/or diagnosis using computed tomography (CT) may contribute to a reduction in the number of deaths caused by pancreatic cancer, similar to the effect of CT screenings on lung cancer [7; 8]. Accurate segmentation of pancreas is the first step in the computer-aided detection/diagnosis system of pancreatic cancer. Compared with conventional techniques of organ segmentation, which use hand-tuned filters and classifiers, deep learning, such as convolutional neural networks (CNN), is a framework, which lets computers learn and build these filters and classifiers from a huge amount of data. Recently, deep learning has been attracting much attention in medical image analysis, as it has been demonstrated as a powerful tool for organ segmentation [9]. Pancreas segmentation using CT images is challenging because the pancreas does not have a distinct border with its surrounding structures. In addition, pancreas has a large shape and size variability among people. Therefore, several different approaches to pancreas segmentation using deep learning have been proposed [10; 11; 12; 13; 14; 15]. Previous studies designed to improve the deep learning model of automatic pancreas segmentation [10; 11; 12; 13; 14; 15] can be classified using three major aspects: (i) dimension of the convolutional network, two-dimensional model (2D) versus three-dimensional model (3D); (ii) use of coarse-scaled model versus fine-scaled model; (iii) improvement of network architecture. In (i), the accuracy of pancreas segmentation was improved in a 3D model and compared with a 2D model; the 3D model makes it possible to fully utilize the 3D spatial information of pancreas, which is useful for grasping the large variability in pancreas shape and size. In (ii), an initial coarse-scaled model was used to obtain a rough region of interest (ROI) of the pancreas, and then the ROI was used for segmentation refinement using a fine-scaled model of pancreas segmentation. The difference in mean dice similarity coefficient (DSC) between the coarse-scaled and find-scaled models ranged from 2% to 7%. In (iii), the network architecture of a deep learning model was modified for efficient segmentation. For example, when an attention unit was introduced in a U-net, the segmentation accuracy was better than in a conventional U-net [12]. In previous studies, the usefulness of data augmentation in pancreas segmentation was not fully evaluated; only conventional methods of data augmentation were utilized. Recently proposed methods of data augmentation, such as mixup [16] and random image cropping and patching (RICAP) [17], were not evaluated. In conventional data augmentation, horizontal flipping, vertical flipping, scaling, rotation, etc., are commonly used. It is necessary to find an effective combination of these, since among the possible combinations, some degrade the performance. Due to the number of the combinations, it is relatively cumbersome to eliminate the counterproductive combinations in conventional data augmentation.