Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images

@article{Seo2020ModifiedU,
  title={Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images},
  author={Hyunseok Seo and Charles Huang and Maxime Bassenne and Ruoxiu Xiao and Lei Xing},
  journal={IEEE Transactions on Medical Imaging},
  year={2020},
  volume={39},
  pages={1316-1325}
}
  • Hyunseok Seo, Charles Huang, +2 authors L. Xing
  • Published 31 October 2019
  • Medicine, Computer Science, Engineering, Mathematics
  • IEEE Transactions on Medical Imaging
Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating organ-at-risks (OARs) and tumor targets have been proposed, automatic segmentation of livers and liver tumors remains intractable due to their low tissue contrast with respect to the surrounding organs… 
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