BLOOD VESSEL SEGMENTATION IN RETINAL IMAGES USING RESVNET ARCHITECTURE
Abstract
The U-Net architecture is often used in medical blood vessel segmentation due to its ability to produce good segmentation. However, U-Net has high complexity due to the presence of the bridge part, which increases the parameters and training time. To overcome this, this research modifies U-Net by removing the bridge part, resulting in V-Net architecture. V-Net architecture faces challenges in capturing deep and complex features. This research proposes modifying V-Net with ResNet architecture in the encoder part, resulting in ResVNet architecture. ResNet, with residual connections, enables the training of very deep networks with more stability and effectiveness in capturing complex features. At the encoder, ResNet is used for more effective training of deep networks and capturing complex features. While at the decoder, U-Net is used to preserve the high resolution and spatial information of the image in segmentation. This study aims to determine the performance evaluation results of the ResVNet architecture. The evaluation measures used are accuracy, sensitivity, precision and Jaccard score. Tests were conducted on the DRIVE and STARE datasets. The measurement results of blood vessel segmentation using ResVNet on the DRIVE dataset resulted in accuracy 96.57%, sensitivity 82.28%, precision 79.57%, and Jaccard score 67.61%. On the STARE dataset, the accuracy results are 96.71%, sensitivity 79.44%, precission 79.44%, and Jaccard score 65.05%. The sensitivity results on the STARE dataset as well as the precision and Jaccard score values on the two datasets produced are still low, in the future this research will make improvements to the ResVNet architecture used.
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J. Lin, X. Huang, H. Zhou, Y. Wang, and Q. Zhang, “Stimulus-guided adaptive transformer network for retinal blood vessel segmentation in fundus images,” Med. Image Anal., vol. 89, p. 102929, 2023, doi: https://doi.org/10.1016/j.media.2023.102929
S. P. Singh, P. Gupta, and R. Dung, “Diabetic retinopathy detection by fundus images using fine tuned deep learning model,” Multimed. Tools Appl., 2024, doi: 10.1007/s11042-024-19687-7.
J. Sanghavi and M. Kurhekar, “Ocular disease detection systems based on fundus images: a survey,” Multimed. Tools Appl., vol. 83, no. 7, pp. 21471–21496, 2024, doi: 10.1007/s11042-023-16366-x.
M. M. Butt, D. N. F. A. Iskandar, S. E. Abdelhamid, G. Latif, and R. Alghazo, “Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features,” Diagnostics, vol. 12, no. 7, 2022, doi: 10.3390/diagnostics12071607.
A. Wijayanti and S. Suryono, “Pengenalan Retina Menggunakan Alihragam Gelombang Singkat dengan Pengukuran Jarak Euclidean Ternormalisasi,” J. Sist. Inf. Bisnis, vol. 4, no. 2, pp. 116–120, 2014, doi: 10.21456/vol4iss2pp116-120.
V. Popat, “GA-based U-Net architecture optimization applied to retina blood vessel segmentation,” IJCCI 2020 - Proceedings of the 12th International Joint Conference on Computational Intelligence. pp. 192–199, 2020. [Online]. Available: https://api.elsevier.com/content/abstract/scopus_id/85103844037
C. Chen, J. H. Chuah, R. Ali, and Y. Wang, “Retinal vessel segmentation using deep learning: A review,” IEEE Access, vol. 9, pp. 111985–112004, 2021, doi: 10.1109/ACCESS.2021.3102176.
O. O. Sule, “A Survey of Deep Learning for Retinal Blood Vessel Segmentation Methods: Taxonomy, Trends, Challenges and Future Directions,” IEEE Access, vol. 10, no. mild, pp. 38202–38236, 2022, doi: 10.1109/ACCESS.2022.3163247.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation BT - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015,” 2015, pp. 234–241.
E. Abdelmaksoud, S. El-Sappagh, S. Barakat, T. Abuhmed, and M. Elmogy, “Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions,” IEEE Access, vol. 9, no. Vl, pp. 15939–15960, 2021, doi: 10.1109/ACCESS.2021.3052870.
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: Learning dense volumetric segmentation from sparse annotation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9901 LNCS, pp. 424–432, 2016, doi: 10.1007/978-3-319-46723-8_49.
W. Weng and X. Zhu, “INet: Convolutional Networks for Biomedical Image Segmentation,” IEEE Access, vol. 9, pp. 16591–16603, 2021, doi: 10.1109/ACCESS.2021.3053408.
A. Reyes-Figueroa, V. H. Flores, and M. Rivera, “Deep neural network for fringe pattern filtering and normalization,” Appl. Opt., vol. 60, no. 7, p. 2022, 2021, doi: 10.1364/ao.413404.
L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, “Rethinking Atrous Convolution for Semantic Image Segmentation,” 2017.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 770–778, 2016, doi: 10.1109/CVPR.2016.90.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity Mappings in Deep Residual Networks,” Lect. Notes Comput. Sci., vol. 9914 LNCS, p. V, 2016, doi: 10.1007/978-3-319-46493-0.
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-ResNet and the impact of residual connections on learning,” 31st AAAI Conf. Artif. Intell. AAAI 2017, pp. 4278–4284, 2017, doi: 10.1609/aaai.v31i1.11231.
N. Komodakis, “Wide Residual Networks,” 2016.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9908 LNCS, pp. 630–645, 2016, doi: 10.1007/978-3-319-46493-0_38.
S. Panchal et al., “Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: A Dataset of Frequently and Rarely Identified Diseases,” Data, vol. 8, no. 2, pp. 1–16, 2023, doi: 10.3390/data8020029.
A. Imran, J. Li, Y. Pei, J. J. Yang, and Q. Wang, “Comparative Analysis of Vessel Segmentation Techniques in Retinal Images,” IEEE Access, vol. 7, pp. 114862–114887, 2019, doi: 10.1109/ACCESS.2019.2935912.
Susila, “Implementasi Edge Detection Pada Citra Grayscale Dengan Metode Operator,” Inf. Sci. Knowl., vol. 12, pp. 235–240, 2017.
Dahliyusmanto, D. W. Anggara, M. S. M. Rahim, and A. W. Ismail, “The Comparison Of Grayscale Image Enhancement Techniques For Improving The Quality Of Marker In Augmented Reality,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 5, pp. 2104–2111, 2021, doi: 10.18517/IJASEIT.11.5.10990.
T. Ohtani, Y. Kanai, and N. V. Kantartzis, “A 4-D subgrid scheme for the NS-FDTD technique using the CNS-FDTD algorithm with the shepard method and a gaussian smoothing filter,” IEEE Trans. Magn., vol. 51, no. 3, pp. 3–6, 2015, doi: 10.1109/TMAG.2014.2360841.
R. S. C. Boss, K. Thangavel, and D. A. P. Daniel, “Automatic Mammogram image Breast Region Extraction and Removal of Pectoral Muscle,” vol. 4, no. 5, 2013, [Online]. Available: http://arxiv.org/abs/1307.7474
G. Yadav, S. Maheshwari, and A. Agarwal, “Contrast limited adaptive histogram equalization based enhancement for real time video system,” Proc. 2014 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2014, pp. 2392–2397, 2014, doi: 10.1109/ICACCI.2014.6968381.
J. Ouyang, S. Liu, H. Peng, H. Garg, and D. N. H. Thanh, “LEA U-Net: a U-Net-based deep learning framework with local feature enhancement and attention for retinal vessel segmentation,” Complex Intell. Syst., 2023, doi: 10.1007/s40747-023-01095-3.
Y. Zhang, “Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation,” Expert Syst. Appl., vol. 195, p. 116526, 2022, doi: 10.1016/j.eswa.2022.116526.
F. Dong, D. Wu, C. Guo, S. Zhang, B. Yang, and X. Gong, “CRAUNet: A cascaded residual attention U-Net for retinal vessel segmentation,” Comput. Biol. Med., vol. 147, no. February, p. 105651, 2022, doi: 10.1016/j.compbiomed.2022.105651.
D. E. Alvarado-Carrillo and O. S. Dalmau-Cedeño, “Width Attention based Convolutional Neural Network for Retinal Vessel Segmentation,” Expert Syst. Appl., vol. 209, p. 118313, 2022, doi: 10.1016/j.eswa.2022.118313.
J. Zhang, Y. Zhang, Y. Jin, J. Xu, and X. Xu, “MDU-Net: multi-scale densely connected U-Net for biomedical image segmentation,” Heal. Inf. Sci. Syst., vol. 11, no. 1, pp. 1–10, 2023, doi: 10.1007/s13755-022-00204-9.
Y. Lv, H. Ma, J. Li, and S. Liu, “Attention Guided U-Net with Atrous Convolution for Accurate Retinal Vessels Segmentation,” IEEE Access, vol. 8, pp. 32826–32839, 2020, doi: 10.1109/ACCESS.2020.2974027.
A. Desiani, Erwin, B. Suprihatin, and S. B. Agustina, “a Robust Techniques of Enhancement and Segmentation Blood Vessels in Retinal Image Using Deep Learning,” Biomed. Eng. - Appl. Basis Commun., vol. 34, no. 4, pp. 1–9, 2022, doi: 10.4015/S1016237222500193.
L. Li, M. Verma, Y. Nakashima, H. Nagahara, and R. Kawasaki, “IterNet: Retinal image segmentation utilizing structural redundancy in vessel networks,” Proc. - 2020 IEEE Winter Conf. Appl. Comput. Vision, WACV 2020, pp. 3645–3654, 2020, doi: 10.1109/WACV45572.2020.9093621.
Q. Xu, Z. Ma, N. HE, and W. Duan, “DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation,” Comput. Biol. Med., vol. 154, no. September 2022, 2023, doi: 10.1016/j.compbiomed.2023.106626.
Y. Tang, Z. Rui, C. Yan, J. Li, and J. Hu, “RESwNet for retinal small vessel segmentation,” IEEE Access, vol. 8, pp. 198265–198274, 2020, doi: 10.1109/ACCESS.2020.3032453.
J. Li, G. Gao, L. Yang, and Y. Liu, “GDF-Net: A multi-task symmetrical network for retinal vessel segmentation,” Biomed. Signal Process. Control, vol. 81, no. August 2022, p. 104426, 2023, doi: 10.1016/j.bspc.2022.104426.
Copyright (c) 2024 Syafira Dian Ramadhani, Erwin Erwin, Anita Desiani, Sinta Bella Agustina
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