Adversarial synthesis learning enables segmentation without target modality ground truth

  title={Adversarial synthesis learning enables segmentation without target modality ground truth},
  author={Yuankai Huo and Zhoubing Xu and Shunxing Bao and Albert Assad and Richard G. Abramson and Bennett A. Landman},
  journal={2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},
A lack of generalizability is one key limitation of deep learning based segmentation. Typically, one manually labels new training images when segmenting organs in different imaging modalities or segmenting abnormal organs from distinct disease cohorts. The manual efforts can be alleviated if one is able to reuse manual labels from one modality (e.g., MRI) to train a segmentation network for a new modality (e.g., CT). Previously, two stage methods have been proposed to use cycle generative… CONTINUE READING
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  • J. Johnson, A. Alahi
  • Fei-Fei, "Perceptual losses for real-time style…
  • 2016
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  • J. Liu, Y. Huo, Z. Xu
  • Assad, et al., "Multi-Atlas Spleen Segmentation…
  • 2017


  • D. Nie, R. Trullo, J. Lian
  • Petitjean, et al., "Medical image synthesis with…
  • 2017
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