AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy.

@article{Zhu2018AnatomyNetDL,
  title={AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy.},
  author={Wentao Zhu and Yufang Huang and Liang Zeng and Xuming Chen and Yong Liu and Z. Qian and Nan Du and Wei Fan and Xiaohui Xie},
  journal={Medical physics},
  year={2018}
}
PURPOSE Radiation therapy (RT) is a common treatment option for head and neck (HaN) cancer. An important step involved in RT planning is the delineation of organs-at-risks (OARs) based on HaN computed tomography (CT). However, manually delineating OARs is time-consuming as each slice of CT images needs to be individually examined and a typical CT consists of hundreds of slices. Automating OARs segmentation has the benefit of both reducing the time and improving the quality of RT planning… CONTINUE READING
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