Reconstruction of the spine structure with bi-planar x-ray images using the generative adversarial network

Chih-Chia Chen; Ting-Yu Su; Wei-Tse Yang; Tsu-Chi Cheng; Yi-Fei He; Cheng-Li Lin; Yu-Hua Fang

doi:10.1117/12.2521601

27 March 2019 Reconstruction of the spine structure with bi-planar x-ray images using the generative adversarial network

Chih-Chia Chen, Ting-Yu Su, Wei-Tse Yang, Tsu-Chi Cheng, Yi-Fei He, Cheng-Li Lin, Yu-Hua Fang

Author Affiliations +

Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110500T (2019) https://doi.org/10.1117/12.2521601
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

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.

Citation Download Citation

Chih-Chia Chen, Ting-Yu Su, Wei-Tse Yang, Tsu-Chi Cheng, Yi-Fei He, Cheng-Li Lin, and Yu-Hua Fang "Reconstruction of the spine structure with bi-planar x-ray images using the generative adversarial network", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500T (27 March 2019); https://doi.org/10.1117/12.2521601

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