1. |
梁远波, 江俊宏. 我国青光眼防治问题与展望. 浙江医学, 2020, 42(22): 2377-2382.
|
2. |
Aquino A, Gegúndez-Arias M E, Marín D. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Transactions on Medical Imaging, 2010, 29(11): 1860-1869.
|
3. |
Cheng J, Liu J, Xu Y, et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Transactions on Medical Imaging, 2013, 32(6): 1019-1032.
|
4. |
Yu T, Ma Y, Li W. Automatic localization and segmentation of optic disc in fundus image using morphology and level set//2015 9th International Symposium on Medical Information and Communication Technology (ISMICT). Kamakura: IEEE, 2015: 195-199.
|
5. |
Issac A, Parthasarthi M, Dutta M K. An adaptive threshold based algorithm for optic disc and cup segmentation in fundus images//2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). Delhi: IEEE, 2015: 143-147.
|
6. |
曹新容, 薛岚燕, 林嘉雯, 等. 基于视觉显著性和旋转扫描的视盘分割新方法. 生物医学工程学杂志, 2018, 35(2): 229-236.
|
7. |
Ramani R G, Shanthamalar J J. Improved image processing techniques for optic disc segmentation in retinal fundus images. Biomedical Signal Processing and Control, 2020, 58: 101832.
|
8. |
Sevastopolsky A. Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognition and Image Analysis, 2017, 27(3): 618-624.
|
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. Munich: Springer, 2015: 234-241.
|
10. |
Al-Bander B, Williams B M, Al-Nuaimy W, et al. Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis. Symmetry, 2018, 10(4): 87.
|
11. |
Fu H, Cheng J, Xu Y, et al. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Transactions on Medical Imaging, 2018, 37(7): 1597-1605.
|
12. |
Yu S, Xiao D, Frost S, et al. Robust optic disc and cup segmentation with deep learning for glaucoma detection. Computerized Medical Imaging and Graphics, 2019, 74: 61-71.
|
13. |
侯向丹, 赵一浩, 刘洪普, 等. 融合残差注意力机制的U-Net视盘分割. 中国图象图形学报, 2020, 25(9): 1915-1929.
|
14. |
吕鹏飞, 王莹, 王思齐, 等. 视觉显著性的眼底图像视盘检测. 中国图象图形学报, 2021, 26(9): 2293-2304.
|
15. |
刘洪普, 赵一浩, 侯向丹, 等. 融合上下文和注意力的视盘视杯分割. 中国图象图形学报, 2021, 26(5): 1041-1057.
|
16. |
刘熠翕, 江旻珊, 张学典. 融合金字塔切分注意力模块的视杯视盘分割. 上海理工大学学报, 2022, 44(6): 532-539, 545.
|
17. |
Sivaswamy J, Krishnadas S, Chakravarty A, et al. A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomedical Imaging Data Papers, 2015, 2(1): 1004.
|
18. |
Sivaswamy J, Krishnadas S R, Joshi G D, et al. DRISHTI-GS: retinal image dataset for optic nerve head (ONH) segmentation//2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). Beijing: IEEE, 2014: 53-56.
|
19. |
Fumero F, Alayón S, Sanchez J L, et al. RIM-ONE: an open retinal image database for optic nerve evaluation//2011 24th International Symposium on Computer-Based Medical Systems (CBMS). Bristol: IEEE, 2011: 1–6.
|
20. |
Gu Z, Cheng J, Fu H, et al. CE-NET: context encoder network for 2D medical image segmentation. IEEE Transactions on Medical Imaging, 2019, 38(10): 2281-2292.
|
21. |
Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation. arXiv preprint, 2017, arXiv: 1706.05587.
|
22. |
Wu T, Tang S, Zhang R, et al. CGNEt: a light-weight context guided network for semantic segmentation. IEEE Transactions on Image Processing, 2020, 30: 1169-1179.
|
23. |
Ni Z L, Bian G B, Zhou X H, et al. RAUNEt: residual attention U-Net for semantic segmentation of cataract surgical instruments//Neural Information Processing: 26th International Conference (ICONIP 2019). Sydney: Springer, 2019: 139-149.
|