ResASPP-UNet: A Modified U-NET Using ResNeT ASPP for Retinal Blood Vessels Segmentation
DOI:
10.33395/sinkron.v10i2.16047Keywords:
SPP, ResNet, Retinal Blood Vessels Segmentation, U-Net.Abstract
In medical imaging, segmenting retinal blood vessels is a crucial task. A ResNet encoder, an ASPP module for multiscale feature extraction, and a UNet decoder comprise the modified U-Net architecture that this work suggests for retinal vascular segmentation. The suggested model extracts the green channel and applies CLAHE during data processing to segment retinal blood vessels. Accuracy, sensitivity, specificity, the Dice coefficient, and Intersection over Union (IoU) are used to assess performance. According to the experimental results, the proposed model obtains an Accuracy, sensitivity, specificity, the Dice coefficient, and IoU of 0.9554, 0.7294, 0.9771, 0.7408, and 0.5884 on the DRIVE dataset and an accuracy of 0.9170 on the DRIVE dataset. Meanwhile, 0.9556, 0.7902, 0.9702, 0.7386, and 0.5865 on the STARE dataset, respectively.
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Abdulsahib, A. A., Mahmoud, M. A., Mohammed, M. A., Rasheed, H. H., Mostafa, S. A., & Maashi, M. S. (2021). Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images. Network Modeling Analysis in Health Informatics and Bioinformatics, 10(1), 20. https://doi.org/10.1007/s13721-021-00294-7
Borawar, L., & Kaur, R. (2023). ResNet: Solving vanishing gradient in deep networks. Proceedings of International Conference on Recent Trends in Computing: ICRTC 2022, 235–247.
Cai, J., Shi, J., Leau, Y.-B., Meng, S., Zheng, X., & Zhou, J. (2024). Res50-SimAM-ASPP-Unet: A Semantic Segmentation Model for High-resolution Remote Sensing Images. IEEE Access, 1–1. https://doi.org/10.1109/ACCESS.2024.3519260
Chen, C., Chuah, J. H., Ali, R., & Wang, Y. (2021). Retinal Vessel Segmentation Using Deep Learning: A Review. IEEE Access, 9, 111985–112004. https://doi.org/10.1109/ACCESS.2021.3102176
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848. https://doi.org/10.1109/TPAMI.2017.2699184
Dash, D., Mahapatra, S., & Agrawal, S. (2024). A Novel Enhanced Res-Unet Model for Retinal Blood Vessels Segmentation. International Conference on Intelligent Computing and Advances in Communication, 57–68.
Duan, Y., Yang, R., Zhao, M., Qi, M., & Peng, S.-L. (2025a). DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation. Mathematics, 13(9), 1454. https://doi.org/10.3390/math13091454
Duan, Y., Yang, R., Zhao, M., Qi, M., & Peng, S.-L. (2025b). DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation. Mathematics, 13(9), 1454. https://doi.org/10.3390/math13091454
Erwin, A. D., Wahyudi, B. S. Y., Cahyono, E. S., & Arhami, M. (2022). BVU-Net: A U-net modification by VGG-batch normalization for retinal blood vessel segmentation. Int. J. Intell. Eng. Syst.
Guo, C., Szemenyei, M., Yi, Y., Wang, W., Chen, B., & Fan, C. (2020). SA-UNET: Spatial attention U-net for retinal vessel segmentation. Proceedings - International Conference on Pattern Recognition, 1236–1242. https://doi.org/10.1109/ICPR48806.2021.9413346
Han, J., Wang, Y., & Gong, H. (2022). Fundus retinal vessels image segmentation method based on improved U-Net. IRBM, 43(6), 628–639.
Hu, L., Zhou, X., Ruan, J., & Li, S. (2024). ASPP+-LANet: A Multi-Scale Context Extraction Network for Semantic Segmentation of High-Resolution Remote Sensing Images. Remote Sensing, 16(6), 1036.
Hu, T., Ding, J., Liu, Y., Zhang, Y., & Yang, L. (2025). DAA‐UNet: A Dense Connectivity and Atrous Spatial Pyramid Pooling Attention UNet Model for Retinal Optical Coherence Tomography Fluid Segmentation. IET Software, 2025(1). https://doi.org/10.1049/sfw2/6006074
Huang, K.-W., Yang, Y.-R., Huang, Z.-H., Liu, Y.-Y., & Lee, S.-H. (2023). Retinal Vascular Image Segmentation Using Improved UNet Based on Residual Module. Bioengineering, 10(6), 722. https://doi.org/10.3390/bioengineering10060722
Huang, L.-K., Tseng, H.-T., Hsieh, C.-C., & Yang, C.-S. (2023). Deep learning based text detection using resnet for feature extraction. Multimedia Tools and Applications, 82(30), 46871–46903. https://doi.org/10.1007/s11042-023-15449-z
Hussain, S., Guo, F., Li, W., & Shen, Z. (2022). DilUnet: A U-net based architecture for blood vessels segmentation. Computer Methods and Programs in Biomedicine, 218, 106732.
Khan, T. M., Alhussein, M., Aurangzeb, K., Arsalan, M., Naqvi, S. S., & Nawaz, S. J. (2020). Residual Connection-Based Encoder Decoder Network (RCED-Net) for Retinal Vessel Segmentation. IEEE Access, 8, 131257–131272. https://doi.org/10.1109/ACCESS.2020.3008899
Lian, S., Li, L., Lian, G., Xiao, X., Luo, Z., & Li, S. (2021). A Global and Local Enhanced Residual U-Net for Accurate Retinal Vessel Segmentation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(3), 852–862. https://doi.org/10.1109/TCBB.2019.2917188
Liu, Y., Fu, Y., & Wen, T. (2024). MobileViTv2-ResUNet: U-shaped transformer architecture for precise retinal vessel segmentation. In Y. Yue (Ed.), International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) (p. 13). SPIE. https://doi.org/10.1117/12.3048337
Liu, Y., Shen, J., Yang, L., Bian, G., & Yu, H. (2023). ResDO-UNet: A deep residual network for accurate retinal vessel segmentation from fundus images. Biomedical Signal Processing and Control, 79, 104087.
Liu, Z., Chen, B., & Zhang, A. (2020). Building segmentation from satellite imagery using U-Net with ResNet encoder. 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 1967–1971. https://doi.org/10.1109/ICMCCE51767.2020.00431
Luo, W., Li, Y., Urtasun, R., & Zemel, R. (2016). Understanding the effective receptive field in deep convolutional neural networks. Advances in Neural Information Processing Systems, 29.
Nisa, S. Q., & Ismail, A. R. (2022). Dual U-Net with Resnet Encoder for Segmentation of Medical Images. International Journal of Advanced Computer Science and Applications, 13(12). https://doi.org/10.14569/IJACSA.2022.0131265
Panchal, S., & Kokare, M. (2024). ResMU-Net: Residual Multi-kernel U-Net for blood vessel segmentation in retinal fundus images. Biomedical Signal Processing and Control, 90, 105859.
Ramadhani, S. D., Erwin, E., Desiani, A., & Bella Agustina, S. (2024). BLOOD VESSEL SEGMENTATION IN RETINAL IMAGES USING RESVNET ARCHITECTURE. Jurnal Teknik Informatika (Jutif), 5(4), 1139–1147. https://doi.org/10.52436/1.jutif.2024.5.4.2637
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 234–241.
Saeedan, F., Weber, N., Goesele, M., & Roth, S. (2018). Detail-preserving pooling in deep networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 9108–9116.
Shi, Z., Wang, T., Huang, Z., Xie, F., Liu, Z., Wang, B., & Xu, J. (2021). MD-Net: A multi-scale dense network for retinal vessel segmentation. Biomedical Signal Processing and Control, 70, 102977. https://doi.org/10.1016/j.bspc.2021.102977
Shi, Z., Wang, T., Xie, F., Huang, Z., Zheng, X., & Zhang, W. (2020). MSU-Net. Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences, 177–181. https://doi.org/10.1145/3429889.3430295
Steinmetz, J. D., Bourne, R. R. A., Briant, P. S., Flaxman, S. R., Taylor, H. R. B., Jonas, J. B., Abdoli, A. A., Abrha, W. A., Abualhasan, A., Abu-Gharbieh, E. G., Adal, T. G., Afshin, A., Ahmadieh, H., Alemayehu, W., Alemzadeh, S. A. S., Alfaar, A. S., Alipour, V., Androudi, S., Arabloo, J., … Vos, T. (2021). Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study. The Lancet Global Health, 9(2), e144–e160. https://doi.org/10.1016/S2214-109X(20)30489-7
Sule, O. O. (2022). A Survey of Deep Learning for Retinal Blood Vessel Segmentation Methods: Taxonomy, Trends, Challenges and Future Directions. IEEE Access, 10, 38202–38236. https://doi.org/10.1109/ACCESS.2022.3163247
Tsai, C.-M., Qiu, S.-H., & Chang, C.-W. (2025). Retinal Vessel Segmentation Using an Attention-Enhanced U-Net Architecture. International Conference on Multimedia Information Technology and Applications, 3–15.
Vijay, S., Guhan, T., Srinivasan, K., Vincent, P. M. D. R., & Chang, C.-Y. (2023). MRI brain tumor segmentation using residual Spatial Pyramid Pooling-powered 3D U-Net. Frontiers in Public Health, 11, 1091850.
Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., & Huang, T. S. (2018). Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7268–7277.
World Health Organization. (2023). Blindness and vision impairment. https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment
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Copyright (c) 2026 Salma Salsabila, Erwin, Anita Desiani
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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