IVCVMar 18, 2020

Oral-3D: Reconstructing the 3D Bone Structure of Oral Cavity from 2D Panoramic X-ray

arXiv:2003.08413v47 citations
AI Analysis

This addresses a domain-specific problem for dentists by enabling 3D visualization from cheaper, lower-radiation 2D X-rays, though it appears incremental as it builds on prior cross-dimension transformation methods.

The paper tackles the problem of reconstructing 3D bone structures of the oral cavity from 2D panoramic X-ray images, proposing Oral-3D, a framework that uses a generative model and deformation module to achieve this transformation, with experimental results showing efficient and effective reconstruction for clinical applications like tooth pulling and dental implants.

Panoramic X-ray (PX) provides a 2D picture of the patient's mouth in a panoramic view to help dentists observe the invisible disease inside the gum. However, it provides limited 2D information compared with cone-beam computed tomography (CBCT), another dental imaging method that generates a 3D picture of the oral cavity but with more radiation dose and a higher price. Consequently, it is of great interest to reconstruct the 3D structure from a 2D X-ray image, which can greatly explore the application of X-ray imaging in dental surgeries. In this paper, we propose a framework, named Oral-3D, to reconstruct the 3D oral cavity from a single PX image and prior information of the dental arch. Specifically, we first train a generative model to learn the cross-dimension transformation from 2D to 3D. Then we restore the shape of the oral cavity with a deformation module with the dental arch curve, which can be obtained simply by taking a photo of the patient's mouth. To be noted, Oral-3D can restore both the density of bony tissues and the curved mandible surface. Experimental results show that Oral-3D can efficiently and effectively reconstruct the 3D oral structure and show critical information in clinical applications, e.g., tooth pulling and dental implants. To the best of our knowledge, we are the first to explore this domain transformation problem between these two imaging methods.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes