3DPX: Progressive 2D-to-3D Oral Image Reconstruction with Hybrid MLP-CNN Networks
<|reference_start|>3DPX: Progressive 2D-to-3D Oral Image Reconstruction with Hybrid MLP-CNN Networks: Panoramic X-ray (PX) is a prevalent modality in dental practice for its wide availability and low cost. However, as a 2D projection image, PX does not contain 3D anatomical information, and therefore has limited use in dental applications that can benefit from 3D information, e.g., tooth angular misa-lignment detection and classification. Reconstructing 3D structures directly from 2D PX has recently been explored to address limitations with existing methods primarily reliant on Convolutional Neural Networks (CNNs) for direct 2D-to-3D mapping. These methods, however, are unable to correctly infer depth-axis spatial information. In addition, they are limited by the in-trinsic locality of convolution operations, as the convolution kernels only capture the information of immediate neighborhood pixels. In this study, we propose a progressive hybrid Multilayer Perceptron (MLP)-CNN pyra-mid network (3DPX) for 2D-to-3D oral PX reconstruction. We introduce a progressive reconstruction strategy, where 3D images are progressively re-constructed in the 3DPX with guidance imposed on the intermediate recon-struction result at each pyramid level. Further, motivated by the recent ad-vancement of MLPs that show promise in capturing fine-grained long-range dependency, our 3DPX integrates MLPs and CNNs to improve the semantic understanding during reconstruction. Extensive experiments on two large datasets involving 464 studies demonstrate that our 3DPX outperforms state-of-the-art 2D-to-3D oral reconstruction methods, including standalone MLP and transformers, in reconstruction quality, and also im-proves the performance of downstream angular misalignment classification tasks.<|reference_end|>
@article{li20243dpx:,
    title={3DPX: Progressive 2D-to-3D Oral Image Reconstruction with Hybrid MLP-CNN
  Networks},
    author={Xiaoshuang Li, Mingyuan Meng, Zimo Huang, Lei Bi, Eduardo Delamare,
  Dagan Feng, Bin Sheng, Jinman Kim},
    journal={arXiv preprint arXiv:2408.01292},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01292},
    primaryClass={eess.IV cs.AI cs.CV}
}