title: Accuracy and repeatability of computer aided cervical vertebra landmarking in cephalogram.
abstract: The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram. 120 adolescents (60 boys, 60 girls) aged from 9.1 to 17.2 years old were randomly selected. Twenty-seven landmarks from the second to fifth cervical vertebrae on the lateral cephalogram were identified. In this study, the system of CACVL was developed and used to identify and calculate the landmarks by fast marching method and parabolic curve fitting. The accuracy and repeatability in CACVL group were compared with those in two manual landmarking groups [orthodontic experts (OE) group and orthodontic novices (ON) group]. The results showed that, as for the accuracy, there was no significant difference between CACVL group and OE group no matter in x-axis or y-axis (P>0.05), but there was significant difference between CACVL group and ON group, as well as OE group and ON group in both axes (P<0.05). As for the repeatability, CACVL group was more reliable than OE group and ON group in both axes. It is concluded that CACVL has the same or higher accuracy, better repeatability and less workload than manual landmarking methods. It's reliable for cervical parameters identification on the lateral cephalogram and cervical vertebral maturation prediction in orthodontic practice and research.
authors.affiliation: Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. Lily-c1030@163.com.
authors.forename: Lili
authors.initials: L
authors.lastname: Chen
authors.affiliation: Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
authors.forename: Zhicong
authors.initials: Z
authors.lastname: Lan
authors.affiliation: Key Laboratory of Education Ministry for Image Processing and Intelligent Control, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430071, China.
authors.forename: Xiangyang
authors.initials: X
authors.lastname: Xu
authors.affiliation: Department of Orthodontics, Peking University School and Hospital of Stomatology and Director, Research Center of Craniofacial Growth and Development, Beijing, 100081, China.
authors.forename: Jiuxiang
authors.initials: J
authors.lastname: Lin
authors.affiliation: Key Laboratory of Education Ministry for Image Processing and Intelligent Control, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430071, China.
authors.forename: Huaifei
authors.initials: H
authors.lastname: Hu
mesh_terms: D000293:Adolescent; D000465:Algorithms; D059925:Anatomic Landmarks / Q000000981:diagnostic imaging*; D002508:Cephalometry / Q000379:methods*; D002574:Cervical Vertebrae / Q000000981:diagnostic imaging*; D002648:Child; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity