Simultaneous Learning of Erector Spinae Muscles for Automatic Segmentation of Site-Specific Skeletal Muscles in Body CT Images

Abstract

Skeletal muscle segmentation of the L3 slice can be used to estimate total body skeletal muscle mass. However, site-specific three-dimensional (3D) segmentation of each region, such as the erector spinae, quadratus lumborum, psoas major, oblique muscle, and rectus abdominis, in the L3 slice has not yet been achieved for the accurate measurement of skeletal muscle mass. Herein, we propose a method for site-specific 3D segmentation of skeletal muscles in the L3 slice from body CT images. We focused on the characteristics of the erector spinae muscle (ESM), which can be simultaneously observed with other skeletal muscles on craniocaudal slices and can be accurately segmented using machine learning. We introduce a segmentation method with ESM (w/ESM) using 2D U-Net for the simultaneous learning of the erector spinae and skeletal muscles, which are recognized as targets. In a three-fold cross-validation using 30 CT image cases, the mean Dice value of the baseline method, without ESM (wo/ESM), was 0.637, whereas that for the segmentation of skeletal muscles by w/ESM was 0.864, an improvement of 0.227. Our method improves the accuracy of site-specific segmentation of skeletal muscles within the L3 slice and helps evaluate the skeletal muscles through 3D imaging. This effect of w/ESM was confirmed for the skeletal muscles within the L3 slice as well as the trapezius and supraspinatus muscles. These results demonstrate the effectiveness of simultaneously learning the erector spinae and skeletal muscles in improving the accuracy of site-specific segmentation of skeletal muscles.

Keywords

• Erector spinae muscles
• L3 slice
• simultaneous learning
• skeletal muscles
• U-Net