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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationInternational Forum on Medical Imaging in Asia 2019
EditorsHiroshi Fujita, Feng Lin, Jong Hyo Kim
PublisherSPIE
ISBN (Electronic)9781510627758
DOIs
Publication statusPublished - 2019 Jan 1
EventInternational Forum on Medical Imaging in Asia 2019 - Singapore, Singapore
Duration: 2019 Jan 72019 Jan 9

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11050
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceInternational Forum on Medical Imaging in Asia 2019
CountrySingapore
CitySingapore
Period19-01-0719-01-09

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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