A fusion network model based on limited training samples for the automatic segmentation of pelvic endangered organs_Journal of Biomedical Engineering

Authors：

WU Qingnan ^1,2 , WANG Yunlai ³ , QUAN Hong ² , WANG Junjie ⁴ , GU Shanshan ³ , YANG Wei ³ , GE Ruigang ³ , LIU Jie ⁵ ,  JU Zhongjian ³

1. Department of Radiation Oncology, Peking University International Hospital, Beijing 102206, P.R.China;
2. School of Physics Science and Technology, Wuhan University, Wuhan 430072, P.R.China;
3. Department of Radiation Oncology, People’s Liberation Army General Hospital, Beijing 100853, P.R.China;
4. Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, P.R.China;
5. Beijing Oriental Ruiyun Technology Corporation, Beijing 100020, P.R.China;

Corresponding author：

JU Zhongjian, Email: 15801234725@163.com

Keywords：

deep learning; multiple model fusion; convolutional neural networks; automatic segmentation; organ at risk delineation

DOI：

10.7507/1001-5515.201809011

Video：

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Abstract Full text Figures/Tables Video References Cited by

When applying deep learning to the automatic segmentation of organs at risk in medical images, we combine two network models of Dense Net and V-Net to develop a Dense V-network for automatic segmentation of three-dimensional computed tomography (CT) images, in order to solve the problems of degradation and gradient disappearance of three-dimensional convolutional neural networks optimization as training samples are insufficient. This algorithm is applied to the delineation of pelvic endangered organs and we take three representative evaluation parameters to quantitatively evaluate the segmentation effect. The clinical result showed that the Dice similarity coefficient values of the bladder, small intestine, rectum, femoral head and spinal cord were all above 0.87 (average was 0.9); Jaccard distance of these were within 2.3 (average was 0.18). Except for the small intestine, the Hausdorff distance of other organs were less than 0.9 cm (average was 0.62 cm). The Dense V-Network has been proven to achieve the accurate segmentation of pelvic endangered organs.

Citation： WU Qingnan, WANG Yunlai, QUAN Hong, WANG Junjie, GU Shanshan, YANG Wei, GE Ruigang, LIU Jie, JU Zhongjian. A fusion network model based on limited training samples for the automatic segmentation of pelvic endangered organs. Journal of Biomedical Engineering, 2020, 37(2): 311-316. doi: 10.7507/1001-5515.201809011 Copy

1.	Duane F K, Langan B, Gillham C, et al. Impact of delineation uncertainties on dose to organs at risk in CT-guided intracavitary brachytherapy. Brachytherapy, 2014, 13(2): 210-218.
2.	Sykes J. Reflections on the current status of commercial automated segmentation systems in clinical practice. J Med Radiat Sci, 2014, 61(3): 131-134.
3.	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.
4.	Cerrolaza J J, Reyes M, Summers R M, et al. Automatic multi-resolution shape modeling of multi-organ structures. Med Image Anal, 2015, 25(1): 11-21.
5.	Okada T, Linguraru M G, Hori M, et al. Abdominal multi-organ CT segmentation using organ correlation graph and prediction-based shape and location priors. Med Image Comput Comput Assist Interv, 2013, 16(Pt 3): 275-282.
6.	Xu Z, Burke R P, Lee C P, et al. Efficient abdominal segmentation on clinically acquired CT with SIMPLE context learning. Med Image Anal, 2015, 24(1): 18-27.
7.	谷珊珊, 田娟秀, 王运来, 等. 基于 MIM 软件模版数据库的病例数对危及器官自动勾画的探讨. 中国医学装备, 2018, 15(3): 1-4.
8.	Xu Zhoubing, Lee C P, Heinrich M P, et al. Evaluation of six registration methods for the human abdomen on clinically acquired CT. IEEE Trans Biomed Eng, 2016, 63(8): 1563-1572.
9.	Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation// International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin: Springer, 2015, 9351: 234-241.
10.	Dou Qi, Yu Lequan, Chen Hao, et al. 3D deeply supervised network for automated segmentation of volumetric medical images. Med Image Anal, 2017, 41: 40-54.
11.	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2015: 770-778.
12.	Huang Gao, Liu Zhuang, van Der Maaten L, et al. Densely connected convolutional networks// 2017 IEEE Conference on Computer Vision & Pattern Recognition. Honolulu: IEEE, 2017: 2261-2269.
13.	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.
14.	Milletari F, Navab N, Ahmadi S A. V-Net: Fully convolutional neural nets for volumetric medical image segmentation// 2016 Fourth International Conference on 3D Vision (3DV). Stanford: IEEE, 2016: 565-571.
15.	Andrews S, Hamarneh G. Multi-region probabilistic dice similarity coefficient using the Aitchison distance and bipartite graph matching. Comp Sci, 2015: 1-8.
16.	Kazemilar S, Balagopal A, Nguyen D, et al. Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning. Biomed Phys Eng Express, 2018, 4(5): 55-73.
17.	Balagopal A, Kazemifar S, Nguyen D, et al. Fully automated organ segmentation in male pelvic CT images. Phys Med Biol, 2018, 63(24): 245015.
18.	Men Kuo, Dai Jianrong, Li Yexiong. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys, 2017, 44(12): 6377-6389.

1. Duane F K, Langan B, Gillham C, et al. Impact of delineation uncertainties on dose to organs at risk in CT-guided intracavitary brachytherapy. Brachytherapy, 2014, 13(2): 210-218.
2. Sykes J. Reflections on the current status of commercial automated segmentation systems in clinical practice. J Med Radiat Sci, 2014, 61(3): 131-134.
3. 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.
4. Cerrolaza J J, Reyes M, Summers R M, et al. Automatic multi-resolution shape modeling of multi-organ structures. Med Image Anal, 2015, 25(1): 11-21.
5. Okada T, Linguraru M G, Hori M, et al. Abdominal multi-organ CT segmentation using organ correlation graph and prediction-based shape and location priors. Med Image Comput Comput Assist Interv, 2013, 16(Pt 3): 275-282.
6. Xu Z, Burke R P, Lee C P, et al. Efficient abdominal segmentation on clinically acquired CT with SIMPLE context learning. Med Image Anal, 2015, 24(1): 18-27.
7. 谷珊珊, 田娟秀, 王运来, 等. 基于 MIM 软件模版数据库的病例数对危及器官自动勾画的探讨. 中国医学装备, 2018, 15(3): 1-4.
8. Xu Zhoubing, Lee C P, Heinrich M P, et al. Evaluation of six registration methods for the human abdomen on clinically acquired CT. IEEE Trans Biomed Eng, 2016, 63(8): 1563-1572.
9. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation// International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin: Springer, 2015, 9351: 234-241.
10. Dou Qi, Yu Lequan, Chen Hao, et al. 3D deeply supervised network for automated segmentation of volumetric medical images. Med Image Anal, 2017, 41: 40-54.
11. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2015: 770-778.
12. Huang Gao, Liu Zhuang, van Der Maaten L, et al. Densely connected convolutional networks// 2017 IEEE Conference on Computer Vision & Pattern Recognition. Honolulu: IEEE, 2017: 2261-2269.
13. 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.
14. Milletari F, Navab N, Ahmadi S A. V-Net: Fully convolutional neural nets for volumetric medical image segmentation// 2016 Fourth International Conference on 3D Vision (3DV). Stanford: IEEE, 2016: 565-571.
15. Andrews S, Hamarneh G. Multi-region probabilistic dice similarity coefficient using the Aitchison distance and bipartite graph matching. Comp Sci, 2015: 1-8.
16. Kazemilar S, Balagopal A, Nguyen D, et al. Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning. Biomed Phys Eng Express, 2018, 4(5): 55-73.
17. Balagopal A, Kazemifar S, Nguyen D, et al. Fully automated organ segmentation in male pelvic CT images. Phys Med Biol, 2018, 63(24): 245015.
18. Men Kuo, Dai Jianrong, Li Yexiong. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys, 2017, 44(12): 6377-6389.

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