PX2Tooth: Reconstructing the 3D Point Cloud Teeth from a Single Panoramic X-ray

TL;DR

This paper introduces PX2Tooth, a two-stage AI framework that reconstructs 3D teeth from a single panoramic X-ray, reducing radiation and costs while achieving high accuracy on a large dataset.

Contribution

The paper presents a novel two-stage method with a segmentation network and a point cloud generation network, trained on a large dataset, improving 3D teeth reconstruction from 2D images.

Findings

Achieved IoU of 0.793, outperforming previous methods.

Constructed a dataset of 499 CBCT and panoramic X-ray pairs.

Demonstrated the potential of AI for digital dentistry applications.

Abstract

Reconstructing the 3D anatomical structures of the oral cavity, which originally reside in the cone-beam CT (CBCT), from a single 2D Panoramic X-ray(PX) remains a critical yet challenging task, as it can effectively reduce radiation risks and treatment costs during the diagnostic in digital dentistry. However, current methods are either error-prone or only trained/evaluated on small-scale datasets (less than 50 cases), resulting in compromised trustworthiness. In this paper, we propose PX2Tooth, a novel approach to reconstruct 3D teeth using a single PX image with a two-stage framework. First, we design the PXSegNet to segment the permanent teeth from the PX images, providing clear positional, morphological, and categorical information for each tooth. Subsequently, we design a novel tooth generation network (TGNet) that learns to transform random point clouds into 3D teeth. TGNet integrates the segmented patch information and introduces a Prior Fusion Module (PFM) to enhance the generation quality, especially in the root apex region. Moreover, we construct a dataset comprising 499 pairs of CBCT and Panoramic X-rays. Extensive experiments demonstrate that PX2Tooth can achieve an Intersection over Union (IoU) of 0.793, significantly surpassing previous methods, underscoring the great potential of artificial intelligence in digital dentistry.