# Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images
## 1 Introduction  
Segmentation of the cranioxillofacial bones, such as the mandible and maxilla, in computed topography (CT) images is one of the crucial steps for generating three-dimensional (3D) models that are required for the diagnosis and treatment planning of cranioxillofacial deformities, craniofacial tumor resection, or free flap reconstruction of the mandible [1, 2]. Additionally, 3D segmentation of organs at risk (OARs) in head and neck (H&N) CT including the mandible is a critical step in radiotherapy planning for H&N cancer treatment [3]. The conventional segmentation task is performed manually using professional software, which is labor-intensive and time-consuming in clinical practice [4, 5]. Additionally, manual segmentation has limitations such as low reproducibility and operator variability. Moreover, accurate segmentation of head CT is challenging owing to the complexity of the anatomical structures, the low contrast of soft tissue, artifacts caused by mental implants, and variations between individual patients [6]. In specific, weak and false edges of condyles appearing in CT images adversely affect the accurate segmentation of the mandible [7]. Figure 1 shows examples of the difficulties in segmenting the mandible and maxilla. Automatic segmentation can improve efficiency and reliability, reducing segmentation time and clinician workload [7]. Numerous studies exist on automatic or semi-automatic segmentation of the mandible from CT scans, including OARs. In the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2015 Head and Neck Auto Segmentation Challenge [8], various approaches were proposed for the segmentation of OARs including the mandible. The use of public datasets, such as the Public Domain Database for Computational Anatomy (PDDCA) version 1.4.1, which was provided for the challenge, and how to evaluate the model performance have been a standard in head CT segmentation research. Most of these approaches utilize atlas-based methods [9] or model-based methods [10]. Atlas-based methods performs segmentation on novel data by image registration using the prior knowledge from the structures of interest [11]. Although atlas-based methods are popular and widely used for anatomy segmentation, they are sensitive to anatomical variations as they use a fixed set of atlases [12]. Moreover, they are computationally expansive and require many minutes to complete one registration task [13]. Statistical model-based methods utilize a statistical appearance model [14]. The models that best represent the shape or appearance variations in the structure of interest, which are obtained from training with a set of images and segmentations, are selected for a new patient image [15]. However, the shape or appearance described by the statistical model is limited to specific shapes, which gives it less flexibility unless large training sets are employed. In some studies, atlas-based and statistical model-based methods have been combined with each other or with another method, leading to various other approaches for automatic mandible segmentation. [PERSON] et al. [16] used a multi-atlas to obtain an initial segmentation of the OAR and an active shape model to refine the initial segmentation. [PERSON] et al. [17] employed anatomic landmarks and prior knowledge for segmentation. [PERSON] et al. [18] proposed a registration-based semi-automatic mandible segmentation pipeline that uses a nonlinear diffeomorphic method to register preprocessed test CT scans on the reference templates. Recently, as convolutional neural networks (CNNs) have become more effective in computer vision, research on deep learning for medical image segmentation has increased exponentially [19]. The first deep learning-based algorithm utilizing a CNN for the segmentation of OARs in H&N CT was proposed by [PERSON] et al. [20], who employed a network  
Figure 1: Difficulties in mandible and maxilla segmentation.