Automatic registration of dental CT and 3D scanned model using deep split jaw and surface curvature. (May 2023)

• Record Type: Journal Article
• Authors: Kim, Minchang; Chung, Minyoung; Shin, Yeong-Gil; Kim, Bohyoung
• Is Part Of: Computer methods and programs in biomedicine. Volume 233(2023)
• Publication Date: 2023-05
• Subjects: Dental computed tomography (CT) -- Scanned surface model -- Registration -- Metal artifacts reduction -- Deep learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2023.107467
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Fast, robust and fully automatic registration method between dental CT data and 3D scanned models from optical scanner Simple and efficient way to split the maxilla (i.e., upper jaw) and mandible (i.e., lower jaw) in the CT data A novel metric that discriminates between highly fluctuating and smoothly varying regions. Registration method that does not require any preprocessing such as extracting iso-surface for tooth crown from CT data. Abstract: Background and objectives: In the medical field, various image registration applications have been studied. In dentistry, the registration of computed tomography (CT) volume data and 3D optically scanned models is essential for various clinical applications, including orthognathic surgery, implant surgical planning, and augmented reality. Our purpose was to present a fully automatic registration method of dental CT data and 3D scanned models. Methods: We use a 2D convolutional neural network to regress a curve splitting the maxilla (i.e., upper jaw) and mandible (i.e., lower jaw) and the points specifying the front and back ends of the crown from the CT data. Using this regressed information, we extract the point cloud and vertices corresponding to the tooth crown from the CT and scanned data, respectively. We introduce a novel metric, called curvature variance of neighbor (CVN), to discriminate between highly fluctuating and smoothly varying regions of the tooth crown. The registration based on CVN enables more accurate fine registration while reducing the effects of metal artifacts. Moreover, the proposed method does not require any preprocessing such as extracting the iso-surface for the tooth crown from the CT data, thereby significantly reducing the computation time. Results: We evaluated the proposed method with the comparison to several promising registration techniques. Our experimental results using three datasets demonstrated that the proposed method exhibited higher registration accuracy (i.e., 2.85, 1.92, and 7.73 times smaller distance errors for individual datasets) and smaller computation time (i.e., 4.12 times faster registration) than one of the state-of-the-art methods. Moreover, the proposed method worked considerably well for partially scanned data, whereas other methods suffered from the unbalancing of information between the CT and scanned data. Conclusions: The proposed method was able to perform fully automatic and highly accurate registration of dental CT data and 3D scanned models, even with severe metal artifacts. In addition, it could achieve fast registration because it did not require any preprocessing for iso-surface reconstruction from the CT data.