Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

@article{Ibragimov2017SegmentationOO,
  title={Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.},
  author={Bulat Ibragimov and Lei Xing},
  journal={Medical physics},
  year={2017},
  volume={44 2},
  pages={547-557}
}
PURPOSE Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software, and interobserver variability. METHODS Convolutional neural networks (CNNs)-a concept from the field of deep learning… CONTINUE READING
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