## 1 Introduction  
Cone beam computed tomography (CBCT) is a state-of-the-art 3D medical imaging technology that employs a cone-shaped X-ray beam to produce high-resolution images of anatomical structures. One of the standout attributes of CBCT is its high spatial resolution, enabling the visualization of fine details, particularly in hard tissues. This feature makes CBCT emerge in orthopedics, where it supports implant planning, joint assessment, and the evaluation of traumatic injuries, ultimately enhancing patient outcomes and delivering personalized care [1]. Additionally, compared to traditional CT imaging, CBCT can deliver detailed images at a significantly lower radiation dose, making it a valuable tool in emergency departments and surgical rooms, particularly for diagnosis and pre-surgical planning [2, 3]. Currently, CBCT is especially beneficial in maxillofacial and oral surgery. For instance, [PERSON] et al. [4] proposed a workflow for dental pre-surgical planning using immersive virtual reality and CBCT data. [PERSON] et al. [5] utilized 3D-printed models derived from CBCT data to treat a rare type of dens invaginatusin a mandibular incisor, highlighting the utility of 3D models in guiding proper treatment. The generation of accurate 3D models relies on precise bone segmentation [6]. Due to the small dimensions of the scanning machine, CBCT technology is particularly feasible, not only for the scanning of the head and maxillofacial district, but also for other extremity anatomical regions such as foot bones, hands and wrists, limbs, and joints. However, bone segmentation in these anatomical regions is challenging. Unlike long bones, extremities and joints exhibit weak bone borders, fluctuating densities of cancellous tissue, and small inter-bone spacing. Furthermore, extremities consist of various tiny, asymmetrically formed structures with different densities, such as those in the hand, wrist, and foot. Additionally, unlike conventional CT, CBCT grayscale values are directly associated with X-ray attenuation and lack the standardization provided by Hounsfield Unit (HU) calibration, introducing nuances and variations in image intensity characteristics, which complicate the segmentation process. The existing literature extensively covers bone segmentation in CBCT, primarily in dental and maxillofacial surgery contexts [7]. Examples of CBCT segmentation for extremities mainly focus on weight-bearing scans of the foot and ankle. However, there is a deficiency of studies related to extremities and joint segmentation from CBCT scans. The U-Net architecture [8] is a widely used convolutional neural network (CNN) architecture for medical imaging segmentation. U-Net has been applied to conventional CT segmentation of various bony structures [9; 10; 11; 12; 13; 14; 15]. In the context of CBCT data, applications of U-Net have been demonstrated in the maxillofacial region. For instance, [PERSON] et al. [16] used U-Net to accurately segment the mandibular canal in CBCT data, and [PERSON] et al. employed DBA-U-Net for maxillary sinus segmentation [17]. The first CNNs developed to segment three-dimensional images acquired using magnetic resonance imaging (MRI) or computed tomography (CT) were trained on two-dimensional image slices [18]. The majority of these CNNs used axial slices as the input [19; 20], due to the high in-plane resolution compared to the slice thickness [21]. CBCT instead offers volumetric high resolution since 3D CBCT images have isotropic voxels, which means that the voxel dimensions are the same in all three spatial dimensions. The axial training does not take into account any 3D information. [PERSON] et al. [22] proposed to train separate CNNs for each of the three orthogonal slices to classify the voxel through a majority voting scheme. This paper proposes a deep learning-based bone segmentation tool for CBCT orthopedic imaging. The aim is to provide an easy-to-use segmentation and 3D modeling workflow for intricate anatomical districts such as ankles, wrists, and joints.