Electronic cleansing by unpaired contrastive learning in non-cathartic laxative-free CT colonography

Over 50,000 people die every year from colorectal cancer in the United States. Early detection and removal of the types of benign pre-cancerous polyps that can develop into cancers would largely prevent these deaths. Non-cathartic laxative-free computed tomographic (CT) colonography has been shown to provide an effective complete colorectal examination that is easy to tolerate by patients. Instead of physical bowel cleansing, a method called electronic cleansing (EC) is used to perform a virtual cleansing of the colon on the acquired CT colonography images. In this preliminary study, we investigated the possibility of using 3D generative adversarial network (GAN) based unpaired contrastive learning to perform EC in laxative-free CT colonography. The unpaired training samples were collected from clinical laxative-free and cathartically cleansed CT colonography cases. The evaluation was performed by testing the model with a number of clinical laxative-free CT colonography cases. Our preliminary results indicate that the 3D GAN-based model was able to learn to perform EC in laxative-free CT colonography. However, we also identified some problems that need to be addressed before the approach can be considered mature enough for clinical application.