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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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 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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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).
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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