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Clinically applicable deep learning framework for organs at risk delineation in CT images

Nature Machine Intelligence volume 1, pages 480–491 (2019)Cite this article

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Abstract

Radiation therapy is one of the most widely used therapies for cancer treatment. A critical step in radiation therapy planning is to accurately delineate all organs at risk (OARs) to minimize potential adverse effects to healthy surrounding organs. However, manually delineating OARs based on computed tomography images is time-consuming and error-prone. Here, we present a deep learning model to automatically delineate OARs in head and neck, trained on a dataset of 215 computed tomography scans with 28 OARs manually delineated by experienced radiation oncologists. On a hold-out dataset of 100 computed tomography scans, our model achieves an average Dice similarity coefficient of 78.34% across the 28 OARs, significantly outperforming human experts and the previous state-of-the-art method by 10.05% and 5.18%, respectively. Our model takes only a few seconds to delineate an entire scan, compared to over half an hour by human experts. These findings demonstrate the potential for deep learning to improve the quality and reduce the treatment planning time of radiation therapy.

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Fig. 1: An illustration of the 28 OARs to be delineated in the head and neck area.
Fig. 2: Overview of Ua-Net.
Fig. 3: Visualization of one randomly selected CT scan from the test set.
Fig. 4: Visualization of a second randomly selected CT scan from the test set.

Data availability

Because of patient privacy, access to the training data in Dataset 1 will be granted on a case by case basis on submission of a request to the corresponding authors. The availability of the train data is subject to review and approval by IRB. The test data in Dataset 1 is available for non-commercial research purpose at https://github.com/uci-cbcl/UaNet#Data. The CT images for Dataset 2 are freely available at https://doi.org/10.7937/K9/TCIA.2015.7AKGJUPZ and https://doi.org/10.7937/K9/TCIA.2017.8oje5q00; we provide the annotated dataset (freely available) for non-commercial use at https://github.com/uci-cbcl/UaNet#Data. Dataset 3 is freely available at http://www.imagenglab.com/newsite/pddca/.

Code availability

Code for the algorithm development, evaluation and statistical analysis is freely available for non-commercial research purposes (https://github.com/uci-cbcl/UaNet).

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Acknowledgements

We acknowledge helpful discussions with D. Chow, W. Zhu and S. Vang. This work was supported by grants from the National Natural Science Foundation of China (no. 81872547), the Natural Science Foundation of Shanghai (no. 18ZR1430800), the Shanghai Jiao Tong University Medical and Engineering Collaborative Research Fund Project (no. YG2016MS24) and Shanghai Municipal Education Commission—Gaofeng Clinical Medicine Grant Support (no. 20181713).

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  1. These authors contributed equally: Hao Tang, Xuming Chen.

Authors and Affiliations

  1. Department of Computer Science, University of California, Irvine, CA, USA

    Hao Tang & Xiaohui Xie

  2. DeepVoxel Inc., Costa Mesa, CA, USA

    Hao Tang, Yang Liu, Zhipeng Lu, Junhua You & Mingzhou Yang

  3. Department of Radiation Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Xuming Chen, Shengyu Yao, Guoqi Zhao, Yi Xu, Tingfeng Chen & Yong Liu

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  1. Hao Tang
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Contributions

Y.L. and X.X. initiated the project. H.T. and X.X. designed the model architecture. H.T., Yang Liu and X.C. conducted validation experiments. X.C., S.Y., G.Z., Y.X., T.C. and Yong Liu created the dataset. H.T., X.C., Yong Liu and X.X. wrote the paper. Z.L., J.Y. and M.Y. contributed to software development.

Corresponding authors

Correspondence to Yong Liu or Xiaohui Xie.

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Competing interests

H.T. interned with and received a stipend from DeepVoxel. X.X. is an advisor to DeepVoxel. Y.L., Z.L., J.Y. and M.Y. are employees of DeepVoxel. None of the other authors declare competing interests.

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Tang, H., Chen, X., Liu, Y. et al. Clinically applicable deep learning framework for organs at risk delineation in CT images. Nat Mach Intell 1, 480–491 (2019). https://doi.org/10.1038/s42256-019-0099-z

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