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.
|