# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review  
Bingjiang Qiu  
13D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; [EMAIL_ADDRESS] (B.Q. ); [EMAIL_ADDRESS] (H.v.d.W. ); [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (H.H.G. ); [EMAIL_ADDRESS] (M.J.H.W.) Hylee van der Wel  
13D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; [EMAIL_ADDRESS] (B.Q. ); [EMAIL_ADDRESS] (H.v.d.W. ); [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (H.H.G. ); [EMAIL_ADDRESS] (M.J.H.W.) [PERSON]  
13D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; [EMAIL_ADDRESS] (B.Q. ); [EMAIL_ADDRESS] (H.v.d.W. ); [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (H.H.G. ); [EMAIL_ADDRESS] (M.J.H.W.) [PERSON]  
14 Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands 14 Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands 14 Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands 15 Medical Imaging Center (MIC), University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; [EMAIL_ADDRESS] (M.J.H.W.) Jiapu Guo  
23D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands 23D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands 23D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9  
parameters [5]. Studies have shown that 3D VSP requires accurate segmentation of the mandible, which is currently performed by medical technicians [4; 6]. Mandible segmentation, especially for 3D VSP, was usually done manually, which is a time-consuming and poorly reproducible process [7; 8]. The mandible is located in the lower part of the facial skeleton and consists of the body, rami, angle, and condyles. The different anatomical regions of the mandible have varying densities, which is especially true if the teeth and surrounding soft tissue are also considered, which makes accurate manual segmentation of the mandible time-consuming and challenging [9]. Because a 3D model of the mandible is necessary for 3D VSP in OMFS, and time is often a limiting factor in the clinical workflow, fast mandible segmentation has become a frequent topic of research in recent decades [7]. Researchers have attempted to automate the segmentation process of the mandible, as well as all kinds of tissues from medical images, to reduce the processing time and also the inter-observer variability [10]. Recent advances in image segmentation [11; 12; 13; 14; 15] have enabled their applications to medical image segmentation, which stimulate progress in automating mandible segmentation. Although significant progress has been made in mandible segmentation, state-of-the-art methods still experience unsatisfactory outcomes, with several challenges to be solved. For oncology segmentation, the segmentation has to be made for an affected bone (with the tumor), which is a different task from segmenting a bone in implantology or for orthognathic purposes. In implantology, many patients have incomplete teeth, which can accordingly lead to resorption of the bone causing shape variation in the mandible among patients. In orthognathic surgery, the population considered varies greatly in shape in addition to asymmetric, which is also a possible challenge for (automatic) segmentation. Figure 1 shows research challenges in automatic segmentation algorithms for the mandible.