Jie Yao1, Wei Zeng2, Tao He3, Shanluo Zhou2, Yi Zhang3, Jixiang Guo4, Wei Tang5. 1. Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, Shaanxi, China State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China College of Stomatology, Sichuan University, Chengdu, Sichuan, China. 2. State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China College of Stomatology, Sichuan University, Chengdu, Sichuan, China. 3. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China. 4. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China. Electronic address: guojixiang@scu.edu.cn. 5. State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China College of Stomatology, Sichuan University, Chengdu, Sichuan, China. Electronic address: mydrtw@vip.sina.com.
Abstract
INTRODUCTION: Cephalometry plays an important role in the diagnosis and treatment of orthodontics and orthognathic surgery. This study intends to develop an automatic landmark location system to make cephalometry more convenient. METHODS: In this study, 512 lateral cephalograms were collected, and 37 landmarks were included. The coordinates of all landmarks in the 512 films were obtained to establish a labeled dataset: 312 were used as a training set, 100 as a validation set, and 100 as a testing set. An automatic landmark location system based on the convolutional neural network was developed. This system consisted of a global detection module and a locally modified module. The lateral cephalogram was first fed into the global module to obtain an initial estimate of the landmark's position, which was then adjusted with the locally modified module to improve accuracy. Mean radial error (MRE) and success detection rate (SDR) within the range of 1-4 mm were used to evaluate the method. RESULTS: The MRE of our validation set was 1.127 ± 1.028 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 45.95%, 89.19%, 97.30%, 97.30%, and 97.30%. The MRE of our testing set was 1.038 ± 0.893 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 54.05%, 91.89%, 97.30%, 100%, 100%, and 100%. CONCLUSIONS: In this study, we proposed a new automatic landmark location system on the basis of the convolutional neural network. The system could detect 37 landmarks with high accuracy. All landmarks are commonly used in clinical practice and could meet the requirements of different cephalometric analysis methods.
INTRODUCTION: Cephalometry plays an important role in the diagnosis and treatment of orthodontics and orthognathic surgery. This study intends to develop an automatic landmark location system to make cephalometry more convenient. METHODS: In this study, 512 lateral cephalograms were collected, and 37 landmarks were included. The coordinates of all landmarks in the 512 films were obtained to establish a labeled dataset: 312 were used as a training set, 100 as a validation set, and 100 as a testing set. An automatic landmark location system based on the convolutional neural network was developed. This system consisted of a global detection module and a locally modified module. The lateral cephalogram was first fed into the global module to obtain an initial estimate of the landmark's position, which was then adjusted with the locally modified module to improve accuracy. Mean radial error (MRE) and success detection rate (SDR) within the range of 1-4 mm were used to evaluate the method. RESULTS: The MRE of our validation set was 1.127 ± 1.028 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 45.95%, 89.19%, 97.30%, 97.30%, and 97.30%. The MRE of our testing set was 1.038 ± 0.893 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 54.05%, 91.89%, 97.30%, 100%, 100%, and 100%. CONCLUSIONS: In this study, we proposed a new automatic landmark location system on the basis of the convolutional neural network. The system could detect 37 landmarks with high accuracy. All landmarks are commonly used in clinical practice and could meet the requirements of different cephalometric analysis methods.