Curvature-Enhanced Implicit Function Network for High-quality Tooth Model Generation from CBCT Images

Abstract

AbstractIn 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.