FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images

@article{Gao2019FocusNetIL,
  title={FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images},
  author={Yunhe Gao and Rui Huang and Ming Chen and Zhe Wang and Jincheng Deng and Yuan-Yuan Chen and Yiwei Yang and Jie Zhang and Chanjuan Tao and Hongsheng Li},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.12056}
}
  • Yunhe Gao, Rui Huang, +7 authors Hongsheng Li
  • Published 2019
  • Computer Science, Engineering
  • ArXiv
  • In this paper, we propose an end-to-end deep neural network for solving the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images. To conduct radiotherapy planning for nasopharyngeal cancer, more than 10 organs-at-risk (normal organs) need to be precisely segmented in advance. However, the size ratio between large and small organs in the head could reach hundreds. Directly using such imbalanced organ annotations to train deep neural networks generally leads… CONTINUE READING

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