# Fully Automated Electronic Cleansing Using CycleGAN in Computed Tomography Colonography
###### Abstract  
In computed tomography colonography (CTC), an electric cleansing technique is used to mix barium with residual fluid, and colon residue is removed by image processing. However, a nonhomogenous mixture of barium and residue may not be properly removed. We developed an electronic cleansing method using CycleGAN, a deep learning technique, to assist diagnosis in CTC. In this method, an original computed tomography (CT) image taken during a CTC examination and a manually cleansed image in which the barium area was manually removed from the original CT image were prepared and converted to an image in which the barium was removed from the original CT image using CycleGAN. In the experiment, the electric cleansing images obtained using the conventional method were compared with those obtained using the proposed method. The average barium cleansing rates obtained by the conventional and proposed methods were 72.3% and 96.3%, respectively. A visual evaluation of the images showed that it was possible to remove only barium without removing the intestinal tract. Furthermore, the extraction of colorectal polyps and early stage cancerous lesions in the colon was performed as in the conventional method. These results indicate that the proposed method using CycleGAN may be useful for accurately visualizing the colon without barium. CT colonography; CycleGAN; deep learning +
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colonoscopy is reduced [5]. In addition, an electronic cleansing (cleansing) process that removes the residue labeled with barium is now available [8]. This technique removes the fecal tagging from the original CT images obtained via CTC using an algorithm that tracks the interior colon and removes regions of high CT values. This is now available on three-dimensional (3D) imaging workstations. In addition, a material discrimination method [11] and methods using deep learning [12] have been proposed for the cleansing process, and evaluations using phantons and other methods have been conducted. One problem with the cleansing process is that in cases of bad fecal tagging, the barium area is not correctly removed [13; 14]. Bad fecal tagging signifies that the residue, residual liquid, and barium are nonuniformly mixed. In addition, a double layer phenomenon often occurs when only barium sinks, and unmixed intestinal fluid layer is formed on the surface. If the residue is badly tagged, the cleansing process becomes difficult and barium may remain in the same form as the lesion. In such cases, it may be difficult to distinguish barium from the lesion, and analysis and reading of the extracted lesion may take a long time. If the cleansing process is not available, it is necessary to disable this function for diagnosis, which complicates the reading of the lesion. To solve this problem, we focused on a domain transformation technique called a generative adversarial network (GAN) [15]. GANs were proposed by [PERSON] et al. in 2014 and are models that deepen learning by having two neural networks: one that generates images and another that determines whether the images are close to the real ones or compete with each other. By training features from the data, nonexistent data can be generated and transformed according to the features of the existing data. Various applications of GAN have been reported in the medical field [16; 17; 18]. [PERSON] et al. generated a large number of CT images of pulmonary nodules using GANs and used them as training data to discriminate between benign and malignant nodules [16]. [PERSON] et al. generated high-resolution lung cell images using the progressive growth of GANs and improved the accuracy of differentiation between benign and malignant lung cancers [17]. [PERSON] et al. generated CT images of lung nodules using Info GAN, which allows the control of image characteristics, and applied them to the histological classification of lung cancer [18]. In this study, we focused on CycleGAN [19], which is a derivative of GAN.