仅用一张全景牙片重建3D牙齿点云，降低辐射风险。
PX2Tooth: Reconstructing the 3D Point Cloud Teeth from a Single Panoramic X-ray
- 分两阶段：先分割牙片中的牙齿，再生成3D点云。
- 在499对数据上实现0.793的交并比，显著优于以往方法。
- 适合需要低辐射牙科影像重建的研究与临床应用。
从单张全景牙片（PX）重建原本存在于锥形束CT（CBCT）中的口腔三维解剖结构，是数字牙科中一项关键但极具挑战的任务，因其可有效降低诊断过程中的辐射风险和治疗成本。然而，现有方法或误差较大，或仅在小于50例的小规模数据集上训练/评估，可信度受限。本文提出PX2Tooth，一种基于两阶段框架的新方法：首先设计PXSegNet，在全景牙片中分割出恒牙，获取每颗牙的位置、形态与类别信息；随后设计新型牙齿生成网络TGNet，将随机点云转化为3D牙齿。TGNet融合分割区域信息，并引入先验融合模块（PFM），显著提升根尖区域生成质量。同时构建了包含499对CBCT与全景牙片的数据集。大量实验表明，PX2Tooth在交并比（IoU）上达到0.793，显著超越先前方法，展现出人工智能在数字牙科中的巨大潜力。
原文摘要 · Abstract (English)
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.
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