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Automatic multi-organ segmentation in thorax CT images using U-Net-GAN
Author(s): 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
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Paper 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.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095010 (13 March 2019); doi: 10.1117/12.2512552
Show Author Affiliations
Yang Lei, Emory Univ. (United States)
Yingzi Liu, Emory Univ. (United States)
Xue Dong, Emory Univ. (United States)
Sibo Tian, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
Xiaojun Jiang, Emory Univ. (United States)
Kristin Higgins, Emory Univ. (United States)
Jonathan J. Beitler, Emory Univ. (United States)
David S. Yu, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Yi Fang, New York Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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