3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation
<|reference_start|>3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation: Radiography is widely used in orthopedics for its affordability and low radiation exposure. 3D reconstruction from a single radiograph, so-called 2D-3D reconstruction, offers the possibility of various clinical applications, but achieving clinically viable accuracy and computational efficiency is still an unsolved challenge. Unlike other areas in computer vision, X-ray imaging's unique properties, such as ray penetration and fixed geometry, have not been fully exploited. We propose a novel approach that simultaneously learns multiple depth maps (front- and back-surface of multiple bones) derived from the X-ray image to computed tomography registration. The proposed method not only leverages the fixed geometry characteristic of X-ray imaging but also enhances the precision of the reconstruction of the whole surface. Our study involved 600 CT and 2651 X-ray images (4 to 5 posed X-ray images per patient), demonstrating our method's superiority over traditional approaches with a surface reconstruction error reduction from 4.78 mm to 1.96 mm. This significant accuracy improvement and enhanced computational efficiency suggest our approach's potential for clinical application.<|reference_end|>
@article{gu20243ddx:,
    title={3DDX: Bone Surface Reconstruction from a Single Standard-Geometry
  Radiograph via Dual-Face Depth Estimation},
    author={Yi Gu, Yoshito Otake, Keisuke Uemura, Masaki Takao, Mazen Soufi, Seiji
  Okada, Nobuhiko Sugano, Hugues Talbot, Yoshinobu Sato},
    journal={arXiv preprint arXiv:2409.16702},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16702},
    primaryClass={eess.IV cs.CV}
}