X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph
Yuan Liang, Weinan Song, Jiawei Yang, Liang Qiu, Kun Wang, Lei He

TL;DR
X2Teeth is a novel deep learning approach that reconstructs detailed 3D dental cavity structures from a single panoramic radiograph, addressing a previously unexplored challenge in dental imaging.
Contribution
The paper introduces X2Teeth, a ConvNet that decomposes 3D teeth reconstruction into localization and shape estimation, with a patch-based training strategy for end-to-end learning.
Findings
Achieves a 3D reconstruction IoU of 0.681.
Outperforms encoder-decoder and retrieval-based methods by over 1.5 times.
Successfully reconstructs detailed 3D structures of entire dental cavities.
Abstract
3D teeth reconstruction from X-ray is important for dental diagnosis and many clinical operations. However, no existing work has explored the reconstruction of teeth for a whole cavity from a single panoramic radiograph. Different from single object reconstruction from photos, this task has the unique challenge of constructing multiple objects at high resolutions. To conquer this task, we develop a novel ConvNet X2Teeth that decomposes the task into teeth localization and single-shape estimation. We also introduce a patch-based training strategy, such that X2Teeth can be end-to-end trained for optimal performance. Extensive experiments show that our method can successfully estimate the 3D structure of the cavity and reflect the details for each tooth. Moreover, X2Teeth achieves a reconstruction IoU of 0.681, which significantly outperforms the encoder-decoder method by $1.71X and the…
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Taxonomy
TopicsDental Radiography and Imaging · 3D Shape Modeling and Analysis · Anatomy and Medical Technology
