Automated segmentation of oblique abdominal muscle based on body cavity segmentation in torso CT images using U-Net

Abstract

The body cavity region contains organs and is an essential region for skeletal muscle segmentation. This study proposes a method to segment body cavity regions using U-Net with focus on the oblique abdominal muscles. The proposed method comprises two steps. First, the body cavity is segmented using U-Net. Subsequently, the abdominal muscles are identified using recognition techniques. This is achieved by removing the segmented body cavity region from the original computerized tomography (CT) images to obtain a simplified CT image for training. In this image, the visceral organ regions are masked by the body cavity; ensuring that the organs therein are excluded from the segmentation target in advance which has been a primary concern in the conventional method of skeletal muscle segmentation. The segmentation accuracies of the body cavity and oblique abdominal muscle in 16 cases were 98.50% and 84.89%, respectively, in terms of the average dice value. Furthermore, it was observed that body cavity information reduced the number of over-extracted pixels by 36.21% in the segmentation of the oblique abdominal muscles adjacent to the body cavity, improving the segmentation accuracy. In future studies, it could be beneficial to examine whether the proposed simplification of CT images by segmentation of body cavities is also effective for abdominal musculoskeletal muscles adjacent to body cavities divided by tendon ends, such as the rectus abdominis.