Joint optic disc and cup segmentation based on residual multi-scale fully convolutional neural network_Journal of Biomedical Engineering

Authors：

YUAN Xin ¹ ,  ZHENG Xiujuan ¹ , JI Bin ^1,2 , LI Miao ¹ , LI Bin ¹

1. Department of Automation, College of Electrical Engineering, Sichuan University, Chengdu 610065, P.R.China;
2. China Mobile (Chengdu) Industrial Research Institute, Chengdu 610041, P.R.China;

Corresponding author：

ZHENG Xiujuan, Email: xiujuanzheng@scu.edu.cn

Keywords：

deep learning; fully convolutional neural network; optic disc segmentation; optic cup segmentation; glaucoma screening

DOI：

10.7507/1001-5515.201909006

Video：

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Glaucoma is the leading cause of irreversible blindness, but its early symptoms are not obvious and are easily overlooked, so early screening for glaucoma is particularly important. The cup to disc ratio is an important indicator for clinical glaucoma screening, and accurate segmentation of the optic cup and disc is the key to calculating the cup to disc ratio. In this paper, a full convolutional neural network with residual multi-scale convolution module was proposed for the optic cup and disc segmentation. First, the fundus image was contrast enhanced and polar transformation was introduced. Subsequently, W-Net was used as the backbone network, which replaced the standard convolution unit with the residual multi-scale full convolution module, the input port was added to the image pyramid to construct the multi-scale input, and the side output layer was used as the early classifier to generate the local prediction output. Finally, a new multi-tag loss function was proposed to guide network segmentation. The mean intersection over union of the optic cup and disc segmentation in the REFUGE dataset was 0.904 0 and 0.955 3 respectively, and the overlapping error was 0.178 0 and 0.066 5 respectively. The results show that this method not only realizes the joint segmentation of cup and disc, but also improves the segmentation accuracy effectively, which could be helpful for the promotion of large-scale early glaucoma screening.

Citation： YUAN Xin, ZHENG Xiujuan, JI Bin, LI Miao, LI Bin. Joint optic disc and cup segmentation based on residual multi-scale fully convolutional neural network. Journal of Biomedical Engineering, 2020, 37(5): 875-884. doi: 10.7507/1001-5515.201909006 Copy

1.	Tham Y C, Li Xiang, Wong T Y, et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology, 2014, 121(11): 2081-2090.
2.	Garway-Heath D F, Hitchings R A. Quantitative evaluation of the optic nerve head in early glaucoma. Br J Ophthalmol, 1998, 82(4): 352-361.
3.	Jonas J B, Bergua A, Schmitz-Valckenberg P, et al. Ranking of optic disc variables for detection of glaucomatous optic nerve damage. Invest Ophthalmol Vis Sci, 2000, 41(7): 1764-1773.
4.	Michael D H O D. Optic disc size, an important consideration in the glaucoma evaluation. Clin Eye Vis Care, 1999, 11(2): 59-62.
5.	Wang Jinke, Cheng Yuanzhi, Guo Changyong, et al. Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images. Int J Comput Assist Radiol Surg, 2016, 11(5): 817-826.
6.	Shi Changfa, Cheng Yuanzhi, Wang Jinke, et al. Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation. Med Image Anal, 2017, 38: 30-49.
7.	Zhang Jianpeng, Xia Yong, Xie Yutong, et al. Classification of medical images in the biomedical literature by jointly using deep and handcrafted visual features. IEEE J Biomed Health Inform, 2018, 22(5): 1521-1530.
8.	Shi Changfa, Cheng Yuanzhi, Liu Fei, et al. A hierarchical local region-based sparse shape composition for liver segmentation in CT scans. Pattern Recognit, 2016, 50: 88-106.
9.	de Oliveira L A Jr, Medeiros H R, Macêdo D, et al. SegNetRes-CRF: A deep convolutional encoder-decoder architecture for semantic image segmentation//2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro: IEEE, 2018, 39(12): 2481-2495.
10.	Guo Z, Li X, Huang H, et al. Deep learning-based image segmentation on multi-modal medical imaging. IEEE Trans Radiat Plasma Med Sci, 2019, 3(2): 162-169.
11.	Liu Shouqiang, Li Miao, Li Min, et al. Research of animals image semantic segmentation based on deep learning. Concurrency and Computation: Practice and Experience, 2020, 32(1): e4892.
12.	Joshi G D, Sivaswamy J, Krishnadas S R. Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans Med Imaging, 2011, 30(6): 1192-1205.
13.	Dehghani A, Moghaddam H A, Mohammad-Shahram M. Optic disc localization in retinal images using histogram matching. EURASIP Journal on Image and Video Processing, 2012, 2012(1): 19.
14.	肖志涛, 邵一婷, 张芳, 等. 基于眼底结构特征的彩色眼底图像视盘定位方法. 中国生物医学工程学报, 2016, 35(3): 257-263.
15.	Almazroa A, Burman R, Raahemifar K, et al. Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey. J Ophthalmol, 2015, 2015: 180972.
16.	郑绍华, 陈健, 潘林, 等. 基于定向局部对比度的眼底图像视盘检测方法. 中国生物医学工程学报, 2014, 33(3): 289-296.
17.	Zheng Yuanjie, Stambolian D, O'brien J, et al. Optic disc and cup segmentation from color fundus photograph using graph cut with priors. Med Image Comput Comput Assist Interv, 2013, 16(Pt 2): 75-82.
18.	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 Trans Med Imaging, 2010, 29(11): 1860-1869.
19.	刘振宇, 汪淼. 改进区域生长算法在视杯图像分割中的应用. 辽宁大学学报: 自然科学版, 2017, 44(2): 105-113.
20.	Cheng Jun, Liu Jiang, Xu Yanwu, et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imaging, 2013, 32(6): 1019-1032.
21.	Sironi A, Turetken E, Lepetit V, et al. Multiscale centerline detection. IEEE Trans Pattern Anal Mach Intell, 2016, 38(7): 1327-1341.
22.	Kamnitsas K, Ledig C, Newcombe V F J, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal, 2017, 36: 61-78.
23.	Shen Wei, Zhou Mu, Yang Feng, et al. Learning from experts: Developing transferable deep features for patient-level lung cancer prediction//Ourselin S, Joskowicz L, Sabuncu M, et al. Medical image computing and computer-assisted intervention: Lecture notes in computer science. ChamI: Springer, 2016, 9901: 124-131.
24.	Song Jiangdian, Yang Caiyun, Fan Li, et al. Lung lesion extraction using a toboggan based growing automatic segmentation approach. IEEE Trans Med Imaging, 2016, 35(1): 337-353.
25.	Ronneberger O, Fischer P, Brox T, et al. U-net: Convolutional networks for biomedical image segmentation//Navab N, Hornegger J, Wells W, et al. Medical image computing and computer-assisted intervention: Lecture notes in computer science. Cham: Springer, 2015: 234-241.
26.	Fu Huazhu, Cheng Jun, Xu Yanwu, et al. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans Med Imaging, 2018, 37(7): 1597-1605.
27.	Mehta R, Sivaswamy J. M-net: A Convolutional Neural Network for deep brain structure segmentation//2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne: IEEE, 2017: 437-440.
28.	Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. UNet++: A nested U-Net architecture for medical image segmentation//Stoyanov D, Taylor Z, Carneiro G, et al. Deep learning in medical image analysis and multimodal learning for clinical decision support. DLMIA 2018, ML-CDS 2018. Lecture notes in computer science. Cham: Springer, 2018, 11045: 3-11.
29.	Yu F, Wang Dequan, Shelhamer E, et al. Deep layer aggregation//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 2403-2412.
30.	Sevastopolsky A. Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recogn Image Anal, 2017, 27(3): 618-624.
31.	Aganj I, Harisinghani M G, Weissleder R, et al. Unsupervised medical image segmentation based on the local center of mass. Sci Rep, 2018, 8(1): 13012.
32.	Zuiderveld K. Contrast limited adaptive histogram equalization. San Diego: Academic Press Professional, Inc, 1994.
33.	Crum W R, Camara O, Hill D L. Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans Med Imaging, 2006, 25(11): 1451-1461.
34.	Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection//Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2980-2988..
35.	Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 4700-4708.
36.	He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
37.	Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning//Thirty-First AAAI Conference on Artificial Intelligence. San Francisco: AAAI, 2017: 1-12.
38.	Gu Zaiwang, Cheng Jun, Fu Huazhu, et al. CE-Net: context encoder network for 2D medical image segmentation. IEEE Trans Med Imaging, 2019, 38(10): 2281-2292.

1. Tham Y C, Li Xiang, Wong T Y, et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology, 2014, 121(11): 2081-2090.
2. Garway-Heath D F, Hitchings R A. Quantitative evaluation of the optic nerve head in early glaucoma. Br J Ophthalmol, 1998, 82(4): 352-361.
3. Jonas J B, Bergua A, Schmitz-Valckenberg P, et al. Ranking of optic disc variables for detection of glaucomatous optic nerve damage. Invest Ophthalmol Vis Sci, 2000, 41(7): 1764-1773.
4. Michael D H O D. Optic disc size, an important consideration in the glaucoma evaluation. Clin Eye Vis Care, 1999, 11(2): 59-62.
5. Wang Jinke, Cheng Yuanzhi, Guo Changyong, et al. Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images. Int J Comput Assist Radiol Surg, 2016, 11(5): 817-826.
6. Shi Changfa, Cheng Yuanzhi, Wang Jinke, et al. Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation. Med Image Anal, 2017, 38: 30-49.
7. Zhang Jianpeng, Xia Yong, Xie Yutong, et al. Classification of medical images in the biomedical literature by jointly using deep and handcrafted visual features. IEEE J Biomed Health Inform, 2018, 22(5): 1521-1530.
8. Shi Changfa, Cheng Yuanzhi, Liu Fei, et al. A hierarchical local region-based sparse shape composition for liver segmentation in CT scans. Pattern Recognit, 2016, 50: 88-106.
9. de Oliveira L A Jr, Medeiros H R, Macêdo D, et al. SegNetRes-CRF: A deep convolutional encoder-decoder architecture for semantic image segmentation//2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro: IEEE, 2018, 39(12): 2481-2495.
10. Guo Z, Li X, Huang H, et al. Deep learning-based image segmentation on multi-modal medical imaging. IEEE Trans Radiat Plasma Med Sci, 2019, 3(2): 162-169.
11. Liu Shouqiang, Li Miao, Li Min, et al. Research of animals image semantic segmentation based on deep learning. Concurrency and Computation: Practice and Experience, 2020, 32(1): e4892.
12. Joshi G D, Sivaswamy J, Krishnadas S R. Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans Med Imaging, 2011, 30(6): 1192-1205.
13. Dehghani A, Moghaddam H A, Mohammad-Shahram M. Optic disc localization in retinal images using histogram matching. EURASIP Journal on Image and Video Processing, 2012, 2012(1): 19.
14. 肖志涛, 邵一婷, 张芳, 等. 基于眼底结构特征的彩色眼底图像视盘定位方法. 中国生物医学工程学报, 2016, 35(3): 257-263.
15. Almazroa A, Burman R, Raahemifar K, et al. Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey. J Ophthalmol, 2015, 2015: 180972.
16. 郑绍华, 陈健, 潘林, 等. 基于定向局部对比度的眼底图像视盘检测方法. 中国生物医学工程学报, 2014, 33(3): 289-296.
17. Zheng Yuanjie, Stambolian D, O'brien J, et al. Optic disc and cup segmentation from color fundus photograph using graph cut with priors. Med Image Comput Comput Assist Interv, 2013, 16(Pt 2): 75-82.
18. 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 Trans Med Imaging, 2010, 29(11): 1860-1869.
19. 刘振宇, 汪淼. 改进区域生长算法在视杯图像分割中的应用. 辽宁大学学报: 自然科学版, 2017, 44(2): 105-113.
20. Cheng Jun, Liu Jiang, Xu Yanwu, et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imaging, 2013, 32(6): 1019-1032.
21. Sironi A, Turetken E, Lepetit V, et al. Multiscale centerline detection. IEEE Trans Pattern Anal Mach Intell, 2016, 38(7): 1327-1341.
22. Kamnitsas K, Ledig C, Newcombe V F J, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal, 2017, 36: 61-78.
23. Shen Wei, Zhou Mu, Yang Feng, et al. Learning from experts: Developing transferable deep features for patient-level lung cancer prediction//Ourselin S, Joskowicz L, Sabuncu M, et al. Medical image computing and computer-assisted intervention: Lecture notes in computer science. ChamI: Springer, 2016, 9901: 124-131.
24. Song Jiangdian, Yang Caiyun, Fan Li, et al. Lung lesion extraction using a toboggan based growing automatic segmentation approach. IEEE Trans Med Imaging, 2016, 35(1): 337-353.
25. Ronneberger O, Fischer P, Brox T, et al. U-net: Convolutional networks for biomedical image segmentation//Navab N, Hornegger J, Wells W, et al. Medical image computing and computer-assisted intervention: Lecture notes in computer science. Cham: Springer, 2015: 234-241.
26. Fu Huazhu, Cheng Jun, Xu Yanwu, et al. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans Med Imaging, 2018, 37(7): 1597-1605.
27. Mehta R, Sivaswamy J. M-net: A Convolutional Neural Network for deep brain structure segmentation//2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne: IEEE, 2017: 437-440.
28. Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. UNet++: A nested U-Net architecture for medical image segmentation//Stoyanov D, Taylor Z, Carneiro G, et al. Deep learning in medical image analysis and multimodal learning for clinical decision support. DLMIA 2018, ML-CDS 2018. Lecture notes in computer science. Cham: Springer, 2018, 11045: 3-11.
29. Yu F, Wang Dequan, Shelhamer E, et al. Deep layer aggregation//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 2403-2412.
30. Sevastopolsky A. Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recogn Image Anal, 2017, 27(3): 618-624.
31. Aganj I, Harisinghani M G, Weissleder R, et al. Unsupervised medical image segmentation based on the local center of mass. Sci Rep, 2018, 8(1): 13012.
32. Zuiderveld K. Contrast limited adaptive histogram equalization. San Diego: Academic Press Professional, Inc, 1994.
33. Crum W R, Camara O, Hill D L. Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans Med Imaging, 2006, 25(11): 1451-1461.
34. Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection//Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2980-2988..
35. Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 4700-4708.
36. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
37. Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning//Thirty-First AAAI Conference on Artificial Intelligence. San Francisco: AAAI, 2017: 1-12.
38. Gu Zaiwang, Cheng Jun, Fu Huazhu, et al. CE-Net: context encoder network for 2D medical image segmentation. IEEE Trans Med Imaging, 2019, 38(10): 2281-2292.

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