## 1 Introduction  
Surface muscle segmentation in whole-body computed tomography (CT) images is essential not only in clinical applications, but also for the anatomical understanding of the human body. The clinical application of surface muscle segmentation includes muscle analysis for amyotrophic lateral sclerosis (ALS), which is an intractable disease characterized by muscular atrophy [1]. A definitive diagnosis of ALS is challenging because effective diagnoses have not been established; exclusion diagnosis is the primary method used [2]. Therefore, the early detection and early differentiation of diseases that cause muscle atrophy are essential. Image feature analysis of skeletal muscle regions has been attempted as an initial study of image discrimination for ALS [1]. In skeletal muscle feature analysis, the texture of skeletal muscles and surface muscle regions of the limbs are analyzed. Analysis of skeletal muscles is limited to the extremities, and it is unknown where the symptoms of ALS appear in the whole body. The segmentation of surface muscles is also crucial for human anatomical structure recognition by computers. Segmentation studies of anatomical structures in various regions of the human body using CT images have been performed. [PERSON] et al. used a fully convolutional network (FCN) [3] and segmented a total of 19 regions from 17 organs and 2 regions of interest from torso CT images with an average matching rate of 87.9% [4]. Additionally, research on the musculoskeletal region has been conducted. First, [PERSON] and colleagues used a two-dimensional (2D) U-Net [5], an effective network for object segmentation, to automatically segment the skeleton from axial sections of whole-body CT slice images at a low dose [6]. As a result, an average dice coefficient (DC) of 0.95 \(\pm\) 0.01 for the 2D method and 0.92 \(\pm\) 0.01 for the 2.5D method were reported. For skeletal muscle segmentation using CT images, model-based methods have been proposed [7]. In a study by [PERSON] et al., a three-dimensional (3D) shape model was used to identify the psoas major muscle, and in recent years, skeletal muscle segmentation using deep learning has been performed [8; 9; 10; 11; 12]. [PERSON] and [PERSON] proposed a site-specific segmentation method for a total of 19 skeletal muscles, in the thigh and hip regions, using deep learning [8; 9]. [PERSON] et al. used a 2D U-Net to identify the psoas major muscle in a low-dose CT [10], and [PERSON] et al. proposed a method for identifying skeletal muscle and fat from 2D slices in abdominal CT images using an improved fully convolutional network (FCN) [11]. [PERSON] et al. identified the erector spinae muscle using an FCN and showed that large-scale skeletal muscle segmentation by deep learning is possible [12]. Notably, in a CT image, the skeletal muscle region, including the surface muscle, has a similar distribution of CT values to the visceral organs. Compared to muscle segmentation using magnetic-resonance imaging (MRI), this is a challenging task. These are among the primary reasons that surface muscle segmentation for the whole body has not been performed (other than that by the authors of this study in a previous study using a 2D U-Net) [13]. As a result, the mean DC, which is the segmentation match rate with the ground truth, was 0.749 (standard deviation [SD]: 0.032) in three experiments for five test cases. Since the CT images used in this study are composed of 3D volumes, we propose a 3D U-Net [14] as the 3D surface muscle segmentation method, which maintains continuity between slices. However, the 3D-based segmentation method using deep learning requires large graphics processing unit (GPU) memory. Therefore, in order to perform 3D deep learning of the skeletal muscles with limited GPU memory, it is necessary to select and limit the image data size properly. Another task involved with the segmentation of surface muscles is gathering the annotation data required for machine learning.