Abstract
In digital dentistry, high-quality tooth models are essential for dental diagnosis and treatment. 3D CBCT images and intra-oral scanning models are widely used in dental clinics to obtain tooth models. However, CBCT image is volumetric data often with limited resolution (about 0.3–1.0 mm spacing), while intra-oral scanning model is high-resolution tooth crown surface (about 0.03 mm spacing) without root information. Hence, dentists usually scan and combine these two modalities of data to build high-quality tooth models, which is time-consuming and easily affected by various patient conditions or acquisition artifacts. To address this problem, we propose a learning-based framework to generate high-quality tooth models with both fine-grained tooth crown details and root information only from CBCT images. Specifically, we first introduce a tooth segmentation network to extract individual teeth from CBCT images. Then, we utilize an implicit function network to generate tooth models at arbitrary resolution in a continuous learning space. Moreover, to capture fine-grained crown details, we further explore a curvature enhancement module in our framework. Experimental results show that our proposed framework outperforms other state-of-the-art methods quantitatively and qualitatively, demonstrating the effectiveness of our method and its potential applicability in clinical practice.
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Fang, Y. et al. (2022). Curvature-Enhanced Implicit Function Network for High-quality Tooth Model Generation from CBCT Images. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13435. Springer, Cham. https://doi.org/10.1007/978-3-031-16443-9_22
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DOI: https://doi.org/10.1007/978-3-031-16443-9_22
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