Examination of the Large Intestine Domain Segmentation Method from a Simple Abdominal CT Image

Abstract

At present, the research and development of computer-aided diagnosis (CAD) is being conducted in various medi cal fields. Because of the difficulty in separating the large intestine from peripheral organs, little development has been carried out in the area of CAD that employs CT. Recently, due to improved endoscopic precision and the widespread use of CT colonography, research has been underway into such areas as applying CAD to discover colorectal cancer. However, endoscopy and CT colonography require advance preparation. CT colonography in particular is difficult to conduct in some cases because of stress placed on the colon when the colon is inflated by injecting carbon dioxide gas. In this study, research and development was conducted into a method for segmenting, the large-intestine region from plain abdominal CT images captured during an abdominal examination. The proposed multi-step extraction method extracts the optimal shape based on the features by binarizing the data while gradually varying the threshold value from high to low. Using this method makes it possible to acquire images for use in CAD development without necessitating colon-stressing CT colonography. The concordance rate of 82％ between the segmentation results of this method and Ground-truth demonstrate this methodʼs segmentation performance.