This study aimed to develop and validate an automated segmentation method for surface muscles using a three-dimensional (3D) U-Net based on selective voxel patches from whole-body computed tomography (CT) images. Our method defined a voxel patch (VP) as the input images, which consisted of 56 slices selected at equal intervals from the whole slices. In training, one VP was used for each case. In the test, multiple VPs were created according to the number of slices in the test case. Segmentation was then performed for each VP and the results of each VP merged. The proposed method achieved a segmentation accuracy mean dice coefficient of 0.900 for 8 cases. Although challenges remain in muscles adjacent to visceral organs and in small muscle areas, VP is useful for surface muscle segmentation using whole-body CT images with limited annotation data. The limitation of our study is that it is limited to cases of muscular disease with atrophy. Future studies should address whether the proposed method is effective for other modalities or using data with different imaging ranges.

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.