Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks

@article{Tang2018SemiAutomaticRL,
  title={Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks},
  author={Youbao Tang and Adam P. Harrison and Mohammadhadi Bagheri and Jing Xiao and Ronald M. Summers},
  journal={ArXiv},
  year={2018},
  volume={abs/1806.09507}
}
Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients. As such, RECIST annotations must be accurate. However, RECIST annotations manually labeled by radiologists require professional knowledge and are time-consuming, subjective, and prone to inconsistency among different observers. To alleviate these problems, we propose a cascaded convolutional neural network based method to semi-automatically label… 

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