Automatic segmentation of head and neck organs at risk based on three-dimensional U-NET deep convolutional neural network_Journal of Biomedical Engineering

Authors：

DAI Xiangkun , WANG Xiaoshen , DU Lehui , MA Na , XU Shouping , CAI Boning , WANG Shuxin , WANG Zhonguo ,  QU Baolin

Department of Radiotherapy, First Medical Center of PLA General Hospital, BeiJing 100853, P.R.China;

Corresponding author：

QU Baolin, Email: qubl6212@sina.com

Keywords：

deep learning; convolutional neural networks; automatic segmentation; organs at risk

DOI：

10.7507/1001-5515.201903052

Video：

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The segmentation of organs at risk is an important part of radiotherapy. The current method of manual segmentation depends on the knowledge and experience of physicians, which is very time-consuming and difficult to ensure the accuracy, consistency and repeatability. Therefore, a deep convolutional neural network (DCNN) is proposed for the automatic and accurate segmentation of head and neck organs at risk. The data of 496 patients with nasopharyngeal carcinoma were reviewed. Among them, 376 cases were randomly selected for training set, 60 cases for validation set and 60 cases for test set. Using the three-dimensional (3D) U-NET DCNN, combined with two loss functions of Dice Loss and Generalized Dice Loss, the automatic segmentation neural network model for the head and neck organs at risk was trained. The evaluation parameters are Dice similarity coefficient and Jaccard distance. The average Dice Similarity coefficient of the 19 organs at risk was 0.91, and the Jaccard distance was 0.15. The results demonstrate that 3D U-NET DCNN combined with Dice Loss function can be better applied to automatic segmentation of head and neck organs at risk.

Citation： DAI Xiangkun, WANG Xiaoshen, DU Lehui, MA Na, XU Shouping, CAI Boning, WANG Shuxin, WANG Zhonguo, QU Baolin. Automatic segmentation of head and neck organs at risk based on three-dimensional U-NET deep convolutional neural network. Journal of Biomedical Engineering, 2020, 37(1): 136-141. doi: 10.7507/1001-5515.201903052 Copy

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1. Berry S L, Ma Rongtao, Boczkowski A, et al. Evaluating inter-campus plan consistency using a knowledge based planning model. Radiother Oncol, 2016, 120(2): 349-355.
2. Fogliata A, Belosi F, Clivio A, et al. On the pre-clinical validation of a commercial model-based optimisation engine: Application to volumetric modulated arc therapy for patients with lung or prostate cancer. Radiother Oncol, 2014, 113(3): 385-391.
3. Chang A T Y, Hung A W M, Cheung F W K, et al. Comparison of planning quality and efficiency between conventional and knowledge-based algorithms in nasopharyngeal cancer patients using intensity modulated radiation therapy. Int J Radiat Oncol Biol Phys, 2016, 95(3): 981-990.
4. Commowick O, Gregoire V, Malandain G. Atlas-based delineation of lymph node levels in head and neck computed tomography images. Radiother Oncol, 2008, 87(2): 281-289.
5. Sims R, Isambert A, Grégoire V, et al. A pre-clinical assessment of an atlas-based automatic segmentation tool for the head and neck. Radiother Oncol, 2009, 93(3): 474-478.
6. Young A V, Wortham A, Wernick I, et al. Atlas-based segmentation improves consistency and decreases time required for contouring postoperative endometrial cancer nodal volumes. Int J Radiat Oncol Biol Phys, 2011, 79(3): 943-947.
7. Fritscher K D, Peroni M, Zaffino P, et al. Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours. Med Phys, 2014, 41(5): 051910.
8. La Macchia M, Fellin F, Amichetti M, et al. Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer. Radiat Oncol, 2012, 7(1): 160-176.
9. Long J, Shelhamer E, Darrell T, et al. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2014, 39(4): 640-651.
10. Roth H R, Lu Le, Seff A, et al. A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. Med Image Comput Comput Assist Interv, 2014, 17(Pt 1): 520-527.
11. Men Kuo, Chen Xinyuan, Zhang Ye, et al. Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images. Front Oncol, 2017, 7: 315.
12. Yang J, Beadle B M, Garden A S, et al. Auto-segmentation of low-risk clinical target volume for head and neck radiation therapy. Pract Radiat Oncol, 2014, 4(1): 31-37.
13. Roth H R, Lu Le, Farag A, et al. DeepOrgan: Multi-level deep convolutional networks for automated pancreas segmentation// 18th International Conference on Medical Computing and Computer-Assisted Intervention (MICCAI 2015). Munich: MICCAI, 2015: 556-564.
14. Ibragimov B, Xing Lei. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys, 2017, 44(2): 547-557.
15. Roth H R, Oda H, Hayashi Y, et al. Hierarchical 3D fully convolutional networks for multi-organ segmentation. arXiv: 1704.06382.
16. Gibson E, Giganti F, Hu Yipeng, et al. Automatic multi-organ segmentation on abdominal CT with dense V-Networks. IEEE Trans Med Imaging, 2018, 37(8): 1822-1834.
17. 门阔, 戴建荣. 利用深度反卷积神经网络自动勾画放疗危及器官. 中国医学物理学杂志, 2018, 35(3): 256-259.
18. Zhu Wentao, Huang Yufang, Zeng Liang, et al. AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med Phys, 2019, 46(2): 576-589.
19. Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation// International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016). Athens: MICCAI, 2016: 424-432.
20. Sudre C H, Li W, Vercauteren T, et al. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations// International Workshop on Deep Learning in Medical Image Analysis (DLMIA 2017). Cham: DLMIA, 2017: 240-248.
21. Nelms B E, Tomé W A, Robinson G, et al. Variations in the contouring of organs at risk: test case from a patient with oropharyngeal cancer. Int J Radiat Oncol Biol Phys, 2012, 82(1): 368-378.
22. 阴晓娟, 胡彩容, 张秀春, 等. 基于图谱库的 ABAS 自动勾画软件在头颈部肿瘤中的可行性研究. 中华放射肿瘤学杂志, 2016, 25(11): 1233-1237.
23. 邓金城, 彭应林, 刘常春, 等. 深度卷积神经网络在放射治疗计划图像分割中的应用. 中国医学物理学杂志, 2018, 35(6): 621-627.
24. 彭应林, 游雁, 韩非, 等. ABAS 软件勾画 OAR 临床前测试重要性研究. 中华放射肿瘤学杂志, 2016, 25(6): 609-614.
25. Chanyavanich V, Das S K, Lee W R, et al. Knowledge-based IMRT treatment planning for prostate cancer. Med Phys, 2011, 38(5): 2515-2522.
26. Zhu Xiaofeng, Ge Yaorong, Li Taoran, et al. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. Med Phys, 2011, 38(2): 719-726.
27. Yuan Lulin, Ge Yaorong, Lee W R, et al. Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans. Med Phys, 2012, 39(11): 6868-6878.
28. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation// International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2015). Munich: MICCAI, 2015: 234-241.
29. Ren Xuhua, Xiang Lei, Nie Dong, et al. Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images. Med Phys, 2018, 45(5): 2063-2075.

Journal of Biomedical Engineering

Automatic segmentation of head and neck organs at risk based on three-dimensional U-NET deep convolutional neural network

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