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

doi:10.1117/12.2512552

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13 March 2019 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

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Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 1095010 (2019) https://doi.org/10.1117/12.2512552
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

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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, and Xiaofeng Yang "Automatic multi-organ segmentation in thorax CT images using U-Net-GAN ", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095010 (13 March 2019); https://doi.org/10.1117/12.2512552

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