Wei Duan1, Yufei Chen1, Qi Zhang2, Xiang Lin2, Xiaoyu Yang1. 1. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China. 2. Department of Endodontics, School and Hospital of Stomatology, Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai 200072, China.
Abstract
OBJECTIVES: The aim of this study was extracting any single tooth from a CBCT scan and performing tooth and pulp cavity segmentation to visualize and to have knowledge of internal anatomy relationships before undertaking endodontic therapy. METHODS: We propose a two-phase deep learning solution for accurate tooth and pulp cavity segmentation. First, the single tooth bounding box is extracted automatically for both single-rooted tooth (ST) and multirooted tooth (MT). It is achieved by using the Region Proposal Network (RPN) with Feature Pyramid Network (FPN) method from the perspective of panorama. Second, U-Net model is iteratively performed for refined tooth and pulp segmentation against two types of tooth ST and MT, respectively. In light of rough data and annotation problems for dental pulp, we design a loss function with a smoothness penalty in the network. Furthermore, the multi-view data enhancement is proposed to solve the small data challenge and morphology structural problems. RESULTS: The experimental results show that the proposed method can obtain an average dice 95.7% for ST, 96.2% for MT and 88.6% for pulp of ST, 87.6% for pulp of MT. CONCLUSIONS: This study proposed a two-phase deep learning solution for fast and accurately extracting any single tooth from a CBCT scan and performing accurate tooth and pulp cavity segmentation. The 3D reconstruction results can completely show the morphology of teeth and pulps, it also provides valuable data for further research and clinical practice.
OBJECTIVES: The aim of this study was extracting any single tooth from a CBCT scan and performing tooth and pulp cavity segmentation to visualize and to have knowledge of internal anatomy relationships before undertaking endodontic therapy. METHODS: We propose a two-phase deep learning solution for accurate tooth and pulp cavity segmentation. First, the single tooth bounding box is extracted automatically for both single-rooted tooth (ST) and multirooted tooth (MT). It is achieved by using the Region Proposal Network (RPN) with Feature Pyramid Network (FPN) method from the perspective of panorama. Second, U-Net model is iteratively performed for refined tooth and pulp segmentation against two types of tooth ST and MT, respectively. In light of rough data and annotation problems for dental pulp, we design a loss function with a smoothness penalty in the network. Furthermore, the multi-view data enhancement is proposed to solve the small data challenge and morphology structural problems. RESULTS: The experimental results show that the proposed method can obtain an average dice 95.7% for ST, 96.2% for MT and 88.6% for pulp of ST, 87.6% for pulp of MT. CONCLUSIONS: This study proposed a two-phase deep learning solution for fast and accurately extracting any single tooth from a CBCT scan and performing accurate tooth and pulp cavity segmentation. The 3D reconstruction results can completely show the morphology of teeth and pulps, it also provides valuable data for further research and clinical practice.
Entities:
Keywords:
Computer-Assisted image processing; Cone-Beam Computed Tomography; Tooth and pulp cavity segmentation; U-Net model
Authors: Dmitry V Tuzoff; Lyudmila N Tuzova; Michael M Bornstein; Alexey S Krasnov; Max A Kharchenko; Sergey I Nikolenko; Mikhail M Sveshnikov; Georgiy B Bednenko Journal: Dentomaxillofac Radiol Date: 2019-03-05 Impact factor: 2.419