Optic cup and disc segmentation model based on linear attention and dual attention_Journal of Biomedical Engineering

Authors：

LAN Zijun ¹ , XIE Jun ¹ ,  GUO Yan ² , ZHANG Zhe ³ , SUN Bin ¹

1. College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Jinzhong, Shanxi 030600, P. R. China;
2. Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing 100029, P. R. China;
3. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, P. R. China;

Corresponding author：

GUO Yan, Email: 43479223@qq.com

Keywords：

Glaucoma; Optic cup and disc segmentation; Linear attention; Dual attention; Knowledge distillation

DOI：

10.7507/1001-5515.202208061

Video：

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Glaucoma is one of blind causing diseases. The cup-to-disc ratio is the main basis for glaucoma screening. Therefore, it is of great significance to precisely segment the optic cup and disc. In this article, an optic cup and disc segmentation model based on the linear attention and dual attention is proposed. Firstly, the region of interest is located and cropped according to the characteristics of the optic disc. Secondly, linear attention residual network-34 (ResNet-34) is introduced as a feature extraction network. Finally, channel and spatial dual attention weights are generated by the linear attention output features, which are used to calibrate feature map in the decoder to obtain the optic cup and disc segmentation image. Experimental results show that the intersection over union of the optic disc and cup in Retinal Image Dataset for Optic Nerve Head Segmentation (DRISHTI-GS) dataset are 0.962 3 and 0.856 4, respectively, and the intersection over union of the optic disc and cup in retinal image database for optic nerve evaluation (RIM-ONE-V3) are 0.956 3 and 0.784 4, respectively. The proposed model is better than the comparison algorithm and has certain medical value in the early screening of glaucoma. In addition, this article uses knowledge distillation technology to generate two smaller models, which is beneficial to apply the models to embedded device.

Citation： LAN Zijun, XIE Jun, GUO Yan, ZHANG Zhe, SUN Bin. Optic cup and disc segmentation model based on linear attention and dual attention. Journal of Biomedical Engineering, 2023, 40(5): 920-927. doi: 10.7507/1001-5515.202208061 Copy

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.

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.

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