###### Abstract  
Since recognizing the location and extent of infarction is essential for diagnosis and treatment, many methods using deep learning have been reported. Generally, deep learning requires a large amount of training data. To overcome this problem, we generated pseudo patient images using CycleGAN, which performed image transformation without paired images. Then, we aimed to improve the extraction accuracy by using the generated images for the extraction of cerebral infarction regions. First, we used CycleGAN for data augmentation. Pseudo-cerebral infarction images were generated from healthy images using CycleGAN. Finally, U-Net was used to segment the cerebral infarction region using CycleGAN-generated images. Regarding the extraction accuracy, the Dice index was 0.553 for U-Net with CycleGAN, which was an improvement over U-Net without CycleGAN. Furthermore, the number of false positives per case was 3.75 for U-Net without CycleGAN and 1.23 for U-Net with CycleGAN, respectively. The number of false positives was reduced by approximately 67% by introducing the CycleGAN-generated images to training cases. These results indicate that utilizing CycleGAN-generated images was effective and facilitated the accurate extraction of the infarcted regions while maintaining the detection rate. cervical infarction; CycleGAN; deep learning Article Automated Extraction of Cerebral Infarction Region in Head MR Image Using Pseudo Cerebral Infarction Image by CycleGAN  
M. Y.  
to include speech recognition, natural language processing, and image recognition [6]. In the field of image recognition, it has been actively used for classification, regression, and segmentation tasks [7]. In particular, segmentation has become an important task in the medical field, to recognize organs and to understand the location and extent of lesions. FCN [8], SegNet [9], and U-Net [10] are commonly used deep learning techniques for segmentation. In particular, U-Net has shown good performance in segmenting medical images, and has been applied to many regions [11; 12; 13]. Since recognizing the location and extent of infarction is essential for diagnosis and treatment, several studies have been reported on the automatic detection and segmentation of cerebral infarction [14; 15; 16; 17; 18; 19; 20]. [PERSON] et al. [14] proposed a method for detecting cerebral infarction in CT images, and [PERSON] et al. [15] proposed a method for segmenting the infarcted cerebral region using a convolutional neural network (CNN). In addition, [PERSON] et al. [16] segmented the cerebral infarct region from a DWI using a CNN. [PERSON] et al. [17] proposed a method for segmenting infarcted regions with U-Net using multiple image sequences. [PERSON] et al. [19] recently proposed automated segmentation of the infarcted region using variational mode decomposition and U-Net. Segmentation performance was evaluated using a total of 239 cases from a public dataset, and the results showed high similarity to the gold standard. Furthermore, [PERSON] et al. [20] proposed stroke lesion detection using a deep learning model for object detection. Generally, deep learning requires a large amount of training data. However, the collection of medical imaging data is sometimes limited by ethical and other issues. Data augmentation is often used to prevent overfitting because of the small amount of data. During data augmentation, the number of images is increased by image manipulations, such as rotation, enlargement, contraction, contrast change, and the addition of noise. However, data augmentation does not significantly change the nature of the lesion or the target structure, and it is not expected to increase the amount of data. To overcome this problem, we focussed on generative adversarial networks (GANs) [21]. GAN is a network model that generates images similar to training image data and was proposed by [PERSON] et al. in 2014.