Abstract
Obvious errors still exist in the segmentation of artery-vein (AV) in retinal optical coherence tomography angiography (OCTA) images, especially near crossover and branch points. It is believed that these errors occur because the existed segmentation method cannot effectively identify the crossover and branch points of AV. In this study, we proposed a Crossover Loss and a Branch Loss (CBAV-Loss), which are two novel structure-preserving loss functions. By restricting the crossover and branch points of arteries and veins, the segmentation accuracy can be improved by correcting the segmentation errors near the crossover and branch points. The experimental results on a manually annotated AV dataset with 400 OCT and OCTA cubes demonstrate that the crossover and branch losses can effectively reduce errors for AV segmentation and preserve vascular connectivity to a certain extent.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kashani, A.H., et al.: Optical coherence tomography angiography: a comprehensive review of current methods and clinical applications. Prog. Retin. Eye Res. 60, 66–100 (2017)
Alam, M., Lim, J.I., Toslak, D., Yao, X.: Differential artery-vein analysis improves the performance of OCTA staging of sickle cell retinopathy. Transl. Vis. Sci. Technol. 8(2), 3 (2019)
Alam, M., Tosklak, D., Lim, J.I., Yao, X.: Color fundus image guided artery-vein differentiation in optical coherence tomography angiography. Invest. Ophthalmol. Visual Sci. 59(12), 4953–4962 (2018)
Ikram, M.K., et al.: Retinal vessel diameters and risk of impaired fasting glucose or diabetes: the Rotterdam study. Diabetes 55(2), 506–510 (2006)
Wong, T.Y., et al.: Retinal arteriolar narrowing and risk of coronary heart disease in men and women: the Atherosclerosis Risk in Communities Study. JAMA 287(9), 1153–1159 (2002)
Abtahi, M., Le, D., Lim, J.I., Yao, X.: MF-AV-Net: an open-source deep learning network with multimodal fusion options for artery-vein segmentation in OCT angiography. Biomed. Opt. Express 13(9), 4870–4888 (2022)
Le, D., Alam, M., Son, T., Lim, J.I., Yao, X.: Deep learning artery-vein classification in OCT angiography. Ophthal. Technol. XXXI. SPIE 11623, 54–60 (2021)
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III, pp. 234–241. Springer International Publishing, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Wang, J., et al.: Reflectance-based projection-resolved optical coherence tomography angiography. Biomed. Opt. Express 8(3), 1536–1548 (2017)
Gao, M., Guo, Y., Hormel, T.T., Sun, J., Hwang, T.S., Jia, Y.: Reconstruction of high-resolution 6 × 6-mm OCT angiograms using deep learning. Biomed. Opt. Express 11(7), 3585–3600 (2020)
Alam, M., et al.: AV-Net: deep learning for fully automated artery-vein classification in optical coherence tomography angiography. Biomed. Opt. Express 11(9), 5249–5257 (2020)
Alam, M.N., Le, D., Yao, X.: Differential artery-vein analysis in quantitative retinal imaging: a review. Quant. Imaging Med. Surg. 11(3), 1102 (2021)
Wendy Aguilar, M., et al.: Graph-based methods for retinal mosaicing and vascular characterization. In: Escolano, F., Vento, M. (eds.) Graph-Based Representations in Pattern Recognition, pp. 25–36. Springer Berlin Heidelberg, Berlin, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72903-7_3
Chrástek, R., et al.: Automated Calculation of Retinal Arteriovenous Ratio for Detection and Monitoring of Cerebrovascular Disease Based on Assessment of Morphological Changes of Retinal Vascular System. MVA, pp. 240–243 (2002)
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)
Jelinek, H.F., et al.: Towards vessel characterization in the vicinity of the optic disc in digital retinal images. Image Vis. Comput. Conf. 2(7) (2005)
Li, H., Hsu, W., Lee, M.L., Wang, H.: A piecewise Gaussian model for profiling and differentiating retinal vessels. In: Proceedings 2003 International Conference on Image Processing (Cat. No. 03CH37429), vol. 1, p. I-1069. IEEE (2003)
Niemeijer, M., van Ginneken, B., Abrà moff, M.D.: Automatic classification of retinal vessels into arteries and veins. Medical Imaging 2009: Computer-Aided Diagnosis. SPIE vol. 7260, pp. 422–429 (2009)
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)
Simó, A., de Ves, E.: Segmentation of macular fluorescein angiographies A statistical approach. Pattern Recogn. 34(4), 795–809 (2001)
Vázquez, S., Barreira, N., Penedo, M., Penas, M., PoseReino, A.: Automatic classification of retinal vessels into arteries and veins. In: 7th International Conference Biomedical Engineering (BioMED 2010), pp. 230–236 (2010)
Vázquez, S.G., et al.: Improving retinal artery and vein classification by means of a minimal path approach. Mach. Vis. Appl. 24(5), 919–930 (2013)
Milletari, F., Navab, N., Ahmadi, S.A.; V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D vision (3DV), pp. 565–571. IEEE (2016)
Ozan, O., et al.: Attention U-Net: Learning Where to Look for the Pancreas. arXiv preprint arXiv:1804.03999Â (2018)
Ming, L., et al.: Ipn-v2 and octa-500: methodology and dataset for retinal image segmentation. arXiv preprint arXiv:2012.07261 (2020)
Yali, J., et al.: Split-spectrum amplitude-decorrelation angiography with optical coherence tomography. Opt. Express 20(4), 4710–4725 (2012)
Acknowledgments
This work was supported by National Science Foundation of China under Grants (62172223, 62072241), and the Fundamental Research Funds for the Central Universities (30921013105).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, Z., Ma, X., Ji, Z., Su, N., Yuan, S., Chen, Q. (2024). CBAV-Loss: Crossover and Branch Losses for Artery-Vein Segmentation in OCTA Images. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14437. Springer, Singapore. https://doi.org/10.1007/978-981-99-8558-6_5
Download citation
DOI: https://doi.org/10.1007/978-981-99-8558-6_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8557-9
Online ISBN: 978-981-99-8558-6
eBook Packages: Computer ScienceComputer Science (R0)