## 1 Introduction  
Deep learning technology has been widely used in recent years for automatic driving, drones, weather forecasting, and games [1, 2, 3, 4]. The deep learning techniques include classification [5], object detection [6], and semantic segmentation. Semantic segmentation has been used to visualize the three-dimensional (3D) anatomical structures of multiple organs in 3D computed tomography images [7], to facilitate the quantitative coronary angiography-based diagnosis of major vessels in X-ray coronary angiography [8], and to quantitate whole breast image analysis in diffusion-weighted images [9]. An applied method using Conditional Generative Adversarial Networks (cGANs) has been reported [10] to be highly accurate for in segmenting peri-knee tissue using magnetic resonance imaging (MRI). These techniques are used in various body parts and modalities. This study focused on semantic segmentation of the triceps surae muscle, which consists of the gastrocnemius and soleus (SOL) muscles. The gastrocnemius is a biceps muscle that comprises the gastrocnemius lateralis (GL) and gastrocnemius medialis (GM). The triceps is an important muscle because it makes activities, such as walking and standing, possible by its repeated contraction and relaxation. Triceps muscle weakness increases the risk of falling and prevents these essential activities [11]. The muscle volume is commonly used as a measure of the muscle size, and it is frequently measured in studies because it correlates with various functional parameters [12]. In a study by [PERSON] et al., the volume of the triceps femoris muscle was calculated to assess the loss of muscle mass with aging [13]. The volume was calculated by the product of the cross-sectional area and slice thickness obtained by manual segmentation using an imaging software; however, this procedure takes a long time [14]. [PERSON] et al. [15] showed that it is now possible to perform semi-automatic volume calculations using the Random Forest classifier for hand muscle segmentation. However, this method is still semi-automatic, and even though it reduces the effort of manual segmentation, it still requires modifications and is significantly time-consuming. In addition, since magnetic resonance imaging of the lower extremities is often performed with thicker slices and more spacing between images, volume calculations using only the acquired images are likely to differ significantly from the actual volume. Therefore, it was believed that by slice interpolating the MRI images and calculating the volume using the interpolated images, this difference from the actual image could be reduced. Many studies have been conducted on the segmentation of quadriceps [16; 17; 18; 19; 20; 21], there have been no previous studies focusing on the segmentation of triceps with deep learning. In a study conducted by [PERSON] et al. [22], the authors performed the segmentation of the medial gastrocnemius, one of the triceps muscles, without deep learning and reported that the average Dice similarity coefficient (DSC) in 25 subjects was 0.55. In the studies of thigh muscle segmentation using deep learning [16; 17; 18; 19], the higher the DSC \(>\) 0.9, the higher the accuracy of muscle detection. Furthermore, in the studies of segmentation of thigh muscles without deep learning [20; 21], there are examples of detection with relatively high accuracy, although not as high compared with studies that have used deep learning. Based on these results, that the triceps could be detected with high accuracy using the deep learning method. In this study, deep learning was used to detect the triceps surae muscle. Then, the devised interpolation method was used to determine the muscle volume and verify the usefulness of the method. The manuscript is structured as follows. Section 2 outlines the preprocessing of the dataset and the methodology and evaluations for transfer learning and the image interpolation method for semantic segmentation.