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Proceedings Paper

Reconstruction of the spine structure with bi-planar x-ray images using the generative adversarial network
Author(s): Chih-Chia Chen; Ting-Yu Su; Wei-Tse Yang; Tsu-Chi Cheng; Yi-Fei He; Cheng-Li Lin; Yu-Hua Fang
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Paper Abstract

In this study, a new computer-aided system was proposed to automatically reconstruct the spine model. The bi-planar EOS X-ray imaging was adopted as the scanning technology, which is capable of a simultaneous capture of bi-planar X-ray images by slot scanning of the whole body using ultra-low radiation doses. High quality and high contrast anteroposterior (AP) and lateral (LAT) X-ray images will be acquired during scanning period and these two radiographs enable a precise three-dimensional reconstruction of vertebrae, pelvis and other parts of the skeletal system. To overcome the timeconsuming issue of spine reconstruction using EOS system, a generative adversarial network (GAN) was applied to reconstruct the entire spine model, which is consist of generator and discriminator and training by unsupervised learning approach. Nowadays, GAN model has already been adopted in the transformation from 2D image to 3D scenes. Therefore, our approach represents a potential alternative for EOS reconstruction while still maintaining a clinically acceptable diagnostic accuracy.

Paper Details

Date Published: 27 March 2019
PDF: 5 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500T (27 March 2019); doi: 10.1117/12.2521601
Show Author Affiliations
Chih-Chia Chen, National Cheng Kung Univ. (Taiwan)
Ting-Yu Su, National Cheng Kung Univ. (Taiwan)
Wei-Tse Yang, National Cheng Kung Univ. (Taiwan)
Tsu-Chi Cheng, National Cheng Kung Univ. (Taiwan)
Yi-Fei He, National Cheng Kung Univ. (Taiwan)
Cheng-Li Lin, National Cheng Kung Univ. Hospital (Taiwan)
Yu-Hua Fang, National Cheng Kung Univ. (Taiwan)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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