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MGA-Net: multiscale global feature aggregation network for arteriovenous classification

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Abstract

Subtle retinal vascular changes and abnormalities can serve as crucial biomarkers for numerous systemic diseases. The classification of retinal arteries and veins within the fundus holds immense significance in the diagnosis and treatment of ocular conditions. Artificial classification is time-consuming, and the prevailing conventional techniques for arteriovenous classification encounter issues, such as confusion in distinguishing overlapping and intersecting blood vessels. Hence, the segmentation of retinal arteries and veins, considering their diverse structural and functional attributes, becomes imperative. In this article, deep learning methods and attention mechanisms are used. We propose a new efficient multi-scale global feature aggregation arteriovenous classification network, MGA-Net. This network consists of a feature enhancement channel attention (FCA) module and an efficient global feature aggregation (GFA) module. It utilizes attention mechanisms to focus on scale, channel, and spatial feature information, suppress features that tend toward the background, and enhance the edge, intersection, and end features of blood vessels, solving problems such as confusion in classification at overlapping intersections of blood vessels. We applied the proposed method to the reference retinal vascular datasets DRIVE-AV, HRF-AV and LES-AV and compared it with six existing networks. The results demonstrated that the sensitivity (Sen) values of MGA-Net were 70.43%, 56.64%, and 55.33%, respectively, which marked a significant improvement of 6.48%, 3.41%, and 3.8% compared to the conventional U-shaped network. The results show that the proposed model can effectively solve the problem of incorrect classification of arteriovenous malformations. This method can be extended to various vascular segmentation tasks and has good visual diagnostic quality.

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All data generated or analyzed during this study are included in this submitted article.

References

  1. Singh, L.K., Khanna, M., Thawkar, S.: A novel hybrid robust architecture for automatic screening of glaucoma using fundus photos, built on feature selection and machine learning-nature driven computing. Expert. Syst. 39(10), 13069 (2022)

    Article  Google Scholar 

  2. Schweitzer, D., Hammer, M., Kraft, J., Thamm, E., Konigsdorffer, E., Strobel, J.: In vivo measurement of the oxygen saturation of retinal vessels in healthy volunteers. IEEE Trans. Biomed. Eng. 46(12), 1454–1465 (1999)

    Article  Google Scholar 

  3. Wong, T.Y., Klein, R., Klein, B.E., Tielsch, J.M., Hubbard, L., Nieto, F.J.: Retinal microvascular abnormalities and their relationship with hypertension, cardiovascular disease, and mortality. Surv. Ophthalmol. 46(1), 59–80 (2001)

    Article  Google Scholar 

  4. Liew, G., Wang, J.J.: Retinal vascular signs: a window to the heart? Rev. Esp. Cardiol. (Engl. Ed.) 64(6), 515–521 (2011)

    Article  Google Scholar 

  5. Cheung, C.Y.-I., Ikram, M.K., Chen, C., Wong, T.Y.: Imaging retina to study dementia and stroke. Prog. Retin. Eye Res. 57, 89–107 (2017)

    Article  Google Scholar 

  6. Xu, X., Ding, W., Abràmoff, M.D., Cao, R.: An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image. Comput. Methods Programs Biomed. 141, 3–9 (2017)

    Article  Google Scholar 

  7. Singh, L.K., Khanna, M., Thawkar, S., Singh, R.: Deep-learning based system for effective and automatic blood vessel segmentation from retinal fundus images. Multimed. Tools Appl. 83(2), 6005–6049 (2024)

    Article  Google Scholar 

  8. Singh, L.K., Garg, H., Khanna, M., Bhadoria, R.S.: An analytical study on machine learning techniques. In: Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications, pp. 137–157. IGI Global (2021)

  9. Rothaus, K., Jiang, X., Rhiem, P.: Separation of the retinal vascular graph in arteries and veins based upon structural knowledge. Image Vis. Comput. 27(7), 864–875 (2009)

    Article  Google Scholar 

  10. Hu, Q., Abràmoff, M.D., Garvin, M.K.: Automated separation of binary overlapping trees in low-contrast color retinal images. In: Medical Image Computing and Computer-Assisted Intervention–ICCAI 2013: 16th International Conference, Nagoya, Japan, Sept 22–26, 2013, Proceedings, Part II 16, pp. 436–443. Springer (2013)

  11. Estrada, R., Allingham, M.J., Mettu, P.S., Cousins, S.W., Tomasi, C., Farsiu, S.: Retinal artery-vein classification via topology estimation. IEEE Trans. Med. Imaging 34(12), 2518–2534 (2015)

    Article  Google Scholar 

  12. Abràmoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)

    Article  Google Scholar 

  13. Grisan, E., Ruggeri, A.: A divide et impera strategy for automatic classification of retinal vessels into arteries and veins. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439), vol. 1, pp. 890–893. IEEE (2003)

  14. Singh, L.K., Khanna, M., Mansukhani, D., Thawkar, S., Singh, R.: Features fusion based novel approach for efficient blood vessel segmentation from fundus images. Multimed. Tools Appl. (2023). https://doi.org/10.1007/s11042-023-17621-x

    Article  Google Scholar 

  15. Saez, M., González-Vázquez, S., González-Penedo, M., Barceló, M.A., Pena-Seijo, M., Tuero, G.C., Pose-Reino, A.: Development of an automated system to classify retinal vessels into arteries and veins. Comput. Methods Programs Biomed. 108(1), 367–376 (2012)

    Article  Google Scholar 

  16. Berger, L., Eoin, H., Cardoso, M.J., Ourselin, S.: An adaptive sampling scheme to efficiently train fully convolutional networks for semantic segmentation. In: Medical Image Understanding and Analysis: 22nd Conference, MIUA 2018, Southampton, UK, July 9–11, 2018, Proceedings, vol. 22, pp. 277–286. Springer (2018)

  17. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, Oct 5–9, 2015, Proceedings, Part III, vol. 18, pp. 234–241. Springer (2015)

  18. Welikala, R., Foster, P., Whincup, P., Rudnicka, A.R., Owen, C.G., Strachan, D., Barman, S.: Automated arteriole and venule classification using deep learning for retinal images from the UK biobank cohort. Comput. Biol. Med. 90, 23–32 (2017)

    Article  Google Scholar 

  19. AlBadawi, S., Fraz, M.: Arterioles and venules classification in retinal images using fully convolutional deep neural network. In: Image Analysis and Recognition: 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018, Proceedings, vol. 15, pp. 659–668. Springer (2018)

  20. Xu, X., Wang, R., Lv, P., Gao, B., Li, C., Tian, Z., Tan, T., Xu, F.: Simultaneous arteriole and venule segmentation with domain-specific loss function on a new public database. Biomed. Opt. Express 9(7), 3153–3166 (2018)

    Article  Google Scholar 

  21. You, A., Kim, J.K., Ryu, I.H., Yoo, T.K.: Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. Eye Vis. 9(1), 1–19 (2022)

    Article  Google Scholar 

  22. Chen, W., Yu, S., Wu, J., Ma, K., Bian, C., Chu, C., Shen, L., Zheng, Y.: Tr-gan: topology ranking GAN with triplet loss for retinal artery/vein classification. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, Oct 4–8, 2020, Proceedings, Part V, vol. 23, pp. 616–625. Springer (2020)

  23. Zhou, Y., Xu, M., Hu, Y., Lin, H., Jacob, J., Keane, P.A., Alexander, D.C.: Learning to address intra-segment misclassification in retinal imaging. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, Sept 27–Oct 1, 2021, Proceedings, Part I, vol. 24, pp. 482–492. Springer (2021)

  24. Duan, K., Wang, S., Liu, H., He, J.: Retinal artery/vein classification based on multi-scale category fusion. In: 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1036–1041. IEEE (2022)

  25. Gao, G., Xu, G., Li, J., Yu, Y., Lu, H., Yang, J.: Fbsnet: a fast bilateral symmetrical network for real-time semantic segmentation. IEEE Trans. Multimed. 25, 3273–3283 (2022)

    Article  Google Scholar 

  26. Meng, C., Sun, K., Guan, S., Wang, Q., Zong, R., Liu, L.: Multiscale dense convolutional neural network for DSA cerebrovascular segmentation. Neurocomputing 373, 123–134 (2020)

    Article  Google Scholar 

  27. Orlando, J.I., Prokofyeva, E., Blaschko, M.B.: A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans. Biomed. Eng. 64(1), 16–27 (2016)

    Article  Google Scholar 

  28. Zhang, M., Zhang, C., Wu, X., Cao, X., Young, G.S., Chen, H., Xu, X.: A neural network approach to segment brain blood vessels in digital subtraction angiography. Comput. Methods Programs Biomed. 185, 105159 (2020)

    Article  Google Scholar 

  29. Malaya, K., Nath, S.: Dandapat: multiscale ICA for fundus image analysis. Int. J. Imaging Syst. Technol. 23(4), 327–337 (2013)

    Article  Google Scholar 

  30. Kar, M.K., Nath, M.K., Neog, D.R.: A review on progress in semantic image segmentation and its application to medical images. SN Comput. Sci. 2(5), 1–30 (2021)

    Article  Google Scholar 

  31. Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)

  32. Ibtehaz, N., Rahman, M.S.: Multiresunet: rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74–87 (2020)

    Article  Google Scholar 

  33. Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2019)

    Article  Google Scholar 

  34. Sanchesa, P., Meyer, C., Vigon, V., Naegel, B.: Cerebrovascular network segmentation of MRA images with deep learning. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 768–771. IEEE (2019)

  35. Kar, M.K., Neog, D.R., Nath, M.K.: Retinal vessel segmentation using multi-scale residual convolutional neural network (MSR-Net) combined with generative adversarial networks. Circuits Syst. Signal Process. 42, 1206–1235 (2023)

    Article  Google Scholar 

  36. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  37. Gao, S.-H., Cheng, M.-M., Zhao, K., Zhang, X.-Y., Yang, M.-H., Torr, P.: Res2net: a new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652–662 (2019)

    Article  Google Scholar 

  38. Hu, J., Wang, H., Cao, Z., Wu, G., Zhang, J.: Automatic artery/vein classification using a vessel-constraint network for multicenter fundus images. Front. Cell Dev. Biol. 9, 659941 (2021)

    Article  Google Scholar 

  39. Elhassan, M.A., Yang, C., Huang, C., Legesse Munea, T., Hong, X.: S-fpn: scale-ware strip attention guided feature pyramid network for real-time semantic segmentation. arXiv e-prints, 2206 (2022)

  40. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534–11542 (2020)

  41. Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices (2017)

  42. Chen, L.-C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: scale-aware semantic image segmentation (2016)

  43. Qureshi, T.A., Habib, M., Hunter, A., Al-Diri, B.: A manually-labeled, artery/vein classified benchmark for the drive dataset. In: Proceedings of the 26th IEEE International Symposium on Computer-based Medical Systems, pp. 485–488. IEEE (2013)

  44. Orlando, J.I., Barbosa Breda, J., Van Keer, K., Blaschko, M.B., Blanco, P.J., Bulant, C.A.: Towards a glaucoma risk index based on simulated hemodynamics from fundus images. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, Sept 16–20, 2018, Proceedings, Part II , vol. 11, pp. 65–73. Springer (2018)

  45. Odstrcilik, J., Kolar, R., Budai, A., Hornegger, J., Jan, J., Gazarek, J., Kubena, T., Cernosek, P., Svoboda, O., Angelopoulou, E.: Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Proc. 7(4), 373–383 (2013)

  46. Anbalagan, T., Nath, M.K., Vijayalakshmi, D., Anbalagan, A.: Analysis of various techniques for ECG signal in healthcare, past, present, and future. Biomed. Eng. Adv. 6, 100089 (2023)

    Article  Google Scholar 

  47. Tomar, N.K., Jha, D., Ali, S., Johansen, H.D., Johansen, D., Riegler, M.A., Halvorsen, P.: Ddanet: Dual decoder attention network for automatic polyp segmentation. In: Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, Jan 10–15, 2021, Proceedings, Part VIII, pp. 307–314. Springer (2021)

  48. Patel, K., Bur, A.M., Wang, G.: Enhanced u-net: a feature enhancement network for polyp segmentation. In: 2021 18th Conference on Robots and Vision (CRV), pp. 181–188. IEEE (2021)

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 81901190.

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Author notes

  1. Ying Cui, Jia Zhu, Liwei Chen and Guang Zhang contributed equally to this work.

Authors and Affiliations

  1. College of Information and Communication Engineering, Harbin Engineering University, Nantong Street, Harbin, 150001, Heilongjiang, China

    Ying Cui, Jia Zhu, Liwei Chen & Shan Gao

  2. Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Nantong Street, Harbin, 150001, Heilongjiang, China

    Ying Cui, Jia Zhu, Liwei Chen & Shan Gao

  3. Department of Neurosurgery, First Affiliated Hospital of Harbin Medical University, Youzheng Street, Harbin, 150001, Heilongjiang, China

    Guang Zhang

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Contributions

YC and JZ designed the framework, implemented the method, and wrote the manuscript. LC and GZ collected and analyze data. SG helped to revise the manuscript language. All authors reviewed the manuscript.

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Correspondence to Shan Gao.

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Cui, Y., Zhu, J., Chen, L. et al. MGA-Net: multiscale global feature aggregation network for arteriovenous classification. SIViP 18, 5563–5577 (2024). https://doi.org/10.1007/s11760-024-03141-0

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