Bimeng Jie1,2, Boxuan Han3, Baocheng Yao1,2, Yi Zhang1,2, Hongen Liao4, Yang He5,6. 1. Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, 22 Zhongguancun South Road, Beijing, 100081, China. 2. National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, National Clinical Research Center for Oral Diseases, Beijing, China. 3. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, 100084, China. 4. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, 100084, China. liao@tsinghua.edu.cn. 5. Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, 22 Zhongguancun South Road, Beijing, 100081, China. fridaydust1983@163.com. 6. National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, National Clinical Research Center for Oral Diseases, Beijing, China. fridaydust1983@163.com.
Abstract
OBJECTIVES: The aim of this study was to propose and validate an automatic approach based on iterative closest point algorithm for virtual complement and reconstruction for maxillofacial bone defects. MATERIALS AND METHODS: A 3D craniomaxillofacial database of normal Chinese people including 500 skull models was established. Modified iterative closest point (ICP) algorithm was developed to complete bone defects automatically. The performances were evaluated by two approaches: (1) model experiment, virtual bony defects were created on 30 intact normal skull models not included in the database. For each defect model, the algorithm was applied to select the reference skull model from the database. 3-Dimensional and 2-dimensional comparison were conducted to evaluate the error between reference skull model with original intact model. Root mean square error (RMSE) and processing time were calculated. (2) Clinical application, the algorithm was utilized to assist reconstruction of 5 patients with maxillofacial bone defects. The symmetry of post-operative skull model was evaluated by comparing with its mirrored model. RESULTS: The algorithm was tested on an CPU with 1.80 GHz and average processing time was 493.5 s. (1) Model experiment, the average root-mean-square deviation of defect area was less than 2 mm. (2) Clinical application, the RMSE of post-operative skull and its mirrored model was 1.72 mm. CONCLUSION: It is feasible using iterative closest point algorithm based on normal people database to automatically predict the reference data of missing maxillofacial bone. CLINICAL RELEVANCE: An automated approach based on ICP algorithm and normal people database for maxillofacial bone defect reconstruction has been proposed and validated.
OBJECTIVES: The aim of this study was to propose and validate an automatic approach based on iterative closest point algorithm for virtual complement and reconstruction for maxillofacial bone defects. MATERIALS AND METHODS: A 3D craniomaxillofacial database of normal Chinese people including 500 skull models was established. Modified iterative closest point (ICP) algorithm was developed to complete bone defects automatically. The performances were evaluated by two approaches: (1) model experiment, virtual bony defects were created on 30 intact normal skull models not included in the database. For each defect model, the algorithm was applied to select the reference skull model from the database. 3-Dimensional and 2-dimensional comparison were conducted to evaluate the error between reference skull model with original intact model. Root mean square error (RMSE) and processing time were calculated. (2) Clinical application, the algorithm was utilized to assist reconstruction of 5 patients with maxillofacial bone defects. The symmetry of post-operative skull model was evaluated by comparing with its mirrored model. RESULTS: The algorithm was tested on an CPU with 1.80 GHz and average processing time was 493.5 s. (1) Model experiment, the average root-mean-square deviation of defect area was less than 2 mm. (2) Clinical application, the RMSE of post-operative skull and its mirrored model was 1.72 mm. CONCLUSION: It is feasible using iterative closest point algorithm based on normal people database to automatically predict the reference data of missing maxillofacial bone. CLINICAL RELEVANCE: An automated approach based on ICP algorithm and normal people database for maxillofacial bone defect reconstruction has been proposed and validated.
Authors: David L Hirsch; Evan S Garfein; Andrew M Christensen; Katherine A Weimer; Pierre B Saddeh; Jamie P Levine Journal: J Oral Maxillofac Surg Date: 2009-10 Impact factor: 1.895
Authors: Gustaaf J C van Baar; Tymour Forouzanfar; Niels P T J Liberton; Henri A H Winters; Frank K J Leusink Journal: Oral Oncol Date: 2018-07-20 Impact factor: 5.337