3D Dental Model Segmentation with Geometrical Boundary Preserving
This work addresses a specific bottleneck in digital dentistry for more precise tooth segmentation, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of low segmentation accuracy at the tooth-gum junction in 3D dental models by proposing CrossTooth, a boundary-preserving method that combines selective downsampling and cross-modal features, resulting in significantly improved accuracy on a public dataset.
3D intraoral scan mesh is widely used in digital dentistry diagnosis, segmenting 3D intraoral scan mesh is a critical preliminary task. Numerous approaches have been devised for precise tooth segmentation. Currently, the deep learning-based methods are capable of the high accuracy segmentation of crown. However, the segmentation accuracy at the junction between the crown and the gum is still below average. Existing down-sampling methods are unable to effectively preserve the geometric details at the junction. To address these problems, we propose CrossTooth, a boundary-preserving segmentation method that combines 3D mesh selective downsampling to retain more vertices at the tooth-gingiva area, along with cross-modal discriminative boundary features extracted from multi-view rendered images, enhancing the geometric representation of the segmentation network. Using a point network as a backbone and incorporating image complementary features, CrossTooth significantly improves segmentation accuracy, as demonstrated by experiments on a public intraoral scan dataset.