Using stacked neural network to improve the auto-segmentation accuracy of Graves’ ophthalmopathy target volumes for radiotherapy_Journal of Biomedical Engineering

Authors：

JIANG Jialiang , ZHOU Li , HE Yisong , JIANG Xiaoxuan ,  FU Yuchuan

Department of Radiotherapy, West China Hospital of Sichuan University, Chengdu 610041, P.R.China;

Corresponding author：

FU Yuchuan, Email: ychfu@hotmail.com

Keywords：

stacked neural network; Graves' ophthalmopathy; automatic segmentation; deep learning

DOI：

10.7507/1001-5515.202002025

Video：

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Compared with the previous automatic segmentation neural network for the target area which considered the target area as an independent area, a stacked neural network which uses the position and shape information of the organs around the target area to regulate the shape and position of the target area through the superposition of multiple networks and fusion of spatial position information to improve the segmentation accuracy on medical images was proposed in this paper. Taking the Graves’ ophthalmopathy disease as an example, the left and right radiotherapy target areas were segmented by the stacked neural network based on the fully convolutional neural network. The volume Dice similarity coefficient (DSC) and bidirectional Hausdorff distance (HD) were calculated based on the target area manually drawn by the doctor. Compared with the full convolutional neural network, the stacked neural network segmentation results can increase the volume DSC on the left and right sides by 1.7% and 3.4% respectively, while the two-way HD on the left and right sides decrease by 0.6. The results show that the stacked neural network improves the degree of coincidence between the automatic segmentation result and the doctor's delineation of the target area, while reducing the segmentation error of small areas. The stacked neural network can effectively improve the accuracy of the automatic delineation of the radiotherapy target area of Graves' ophthalmopathy.

Citation： JIANG Jialiang, ZHOU Li, HE Yisong, JIANG Xiaoxuan, FU Yuchuan. Using stacked neural network to improve the auto-segmentation accuracy of Graves’ ophthalmopathy target volumes for radiotherapy. Journal of Biomedical Engineering, 2020, 37(4): 670-675. doi: 10.7507/1001-5515.202002025 Copy

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1. Litjens G, Kooi T, Bejnordi B E, <italic>et al</italic>. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
2. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston: IEEE. 2015: 3431-3440.
3. 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.
4. Lustberg T, van Soest J, Gooding M, <italic>et al</italic>. Clinical evaluation of Atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol, 2018, 126(2): 312-317.
5. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation, Computer Vision and Pattern Recognition, 2015. arXiv: 1505.04597.
6. Hwee J, Louie A V, Gaede S, <italic>et al</italic>. Technology assessment of automated atlas based segmentation in prostate bed contouring. Radiat Oncol, 2011, 6(1): 110.
7. Kosmin M, Ledsam J, Romera-Paredes B, <italic>et al</italic>. Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer. Radiother Oncol, 2019, 135: 130-140.
8. 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. Medical Physics, 2017, 44(12): 6377-6389.
9. Mourits M P, van Kempen-Harteveld M L, García M B, <italic>et al</italic>. Radiotherapy for graves' orbitopathy: randomised placebo-controlled study. Lancet, 2000, 355(9214): 1505-1509.
10. Zeng L, Xie X Q, Li C H, <italic>et al</italic>. Clinical study of the radiotherapy with EDGE accelerator in the treatment of the moderate and severe thyroid associated ophthalmopathy. Eur Rev Med Pharmacol Sci, 2019, 23(8): 3471-3477.
11. Jia Y, Shelhamer E, Donahue J, et al. Caffe: convolutional architecture for fast feature embedding. Computer Vision and Pattern Recognition, 2014. arXiv: 1408.5093.
12. Ravishankar H, Sudhakar P, Venkataramani R, <italic>et al</italic>. Understanding the mechanisms of deep transfer learning for medical images. Deep Learning in Medical Image Analysis (DLMIA 2016), Lecture Notes in Computer Science (LNCS), Cham: Springer, 2016, 10008: 188-196.
13. Sharp G, Fritscher K D, Pekar V, <italic>et al</italic>. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys, 2014, 41(5): 050902.
14. 何奕松, 蒋家良, 余行, 等. 影像分割技术中 DSC 和 Hausdorff 两种轮廓相似性系数的比较研究. 中国医学物理学杂志, 2019, 36(11): 1307-1311.
15. Popovic A, de la Fuente M, Engelhardt M, <italic>et al</italic>. Statistical validation metric for accuracy assessment in medical image segmentation. Int J Comput Assist Radiol Surg, 2007, 2(3-4): 169-181.
16. Helmut A, Ludmila S. Computing the Hausdorff distance between curved objects. Int J Comput Geom Appl, 2008, 18(4): 307-320.
17. Walker G V, Awan M, Tao R, <italic>et al</italic>. Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer. Radiother Oncol, 2014, 112(3): 321-325.
18. Sykes J. Reflections on the current status of commercial automated segmentation systems in clinical practice. Journal of medical radiation sciences, 2014, 61(3): 131-134.
19. Chen L C, Papandreou G, Kokkinos I A, <italic>et al</italic>. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell, 2018, 40(4): 834-848.
20. Shuai Zheng, Jayasumana S, Romera-Paredes B, et al. Conditional random fields as recurrent neural networks//2015 IEEE International Conference on Computer Vision (ICCV), 2015, 2015: 1529–1537.

Journal of Biomedical Engineering

Using stacked neural network to improve the auto-segmentation accuracy of Graves’ ophthalmopathy target volumes for radiotherapy

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