Automatic multi-organ segmentation in thorax CT images using U-Net-GAN

Yang Lei, Yingzi Liu, Xue Dong, Sibo Tian, Tonghe Wang, Xiaojun Jiang, Kristin Higgins, Jonathan J. Beitler, David S. Yu, Tian Liu, Walter J. Curran, Yi Fang, Xiaofeng Yang

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

Abstract

We propose a method to automatically segment multiple organs at risk (OARs) from routinely-acquired thorax CT images using generative adversarial network (GAN). Multi-label U-Net was introduced in generator to enable end-to-end segmentation. Esophagus and spinal cord location information were used to train the GAN in specific regions of interest (ROI). The probability maps of new CT thorax multi-organ were generated by the well-trained network and fused to reconstruct the final contour. This proposed algorithm was evaluated using 20 patients' data with thorax CT images and manual contours. The mean Dice similarity coefficient (DSC) for esophagus, heart, left lung, right lung and spinal cord was 0.73±0.04, 0.85±0.02, 0.96±0.01, 0.97±0.02 and 0.88±0.03. This novel deep-learning-based approach with the GAN strategy can automatically and accurately segment multiple OARs in thorax CT images, which could be a useful tool to improve the efficiency of the lung radiotherapy treatment planning.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Horst K. Hahn
PublisherSPIE
ISBN (Electronic)9781510625471
DOIs
StatePublished - 2019
EventMedical Imaging 2019: Computer-Aided Diagnosis - San Diego, United States
Duration: Feb 17 2019Feb 20 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10950
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Computer-Aided Diagnosis
CountryUnited States
CitySan Diego
Period2/17/192/20/19

Keywords

  • CT thorax multi-organ
  • End-to-end
  • Generative adversarial network
  • U-Net

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Lei, Y., Liu, Y., Dong, X., Tian, S., Wang, T., Jiang, X., Higgins, K., Beitler, J. J., Yu, D. S., Liu, T., Curran, W. J., Fang, Y., & Yang, X. (2019). Automatic multi-organ segmentation in thorax CT images using U-Net-GAN. In K. Mori, & H. K. Hahn (Eds.), Medical Imaging 2019: Computer-Aided Diagnosis [1095010] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10950). SPIE. https://doi.org/10.1117/12.2512552