• Laser & Optoelectronics Progress
  • Vol. 62, Issue 2, 0217003 (2025)
Linfeng Kong1,2,* and Yun Wu1,2
Author Affiliations
  • 1State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, Guizhou , China
  • 2College of Computer Science and Technology, Guizhou University, Guiyang 550025, Guizhou , China
  • show less
    DOI: 10.3788/LOP241105 Cite this Article Set citation alerts
    Linfeng Kong, Yun Wu. Retinal Vessel Segmentation Using Multi-Directional Stripe Convolution and Pyramid Dual Pooling[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0217003 Copy Citation Text show less
    References

    [1] Fraz M M, Remagnino P, Hoppe A et al. Blood vessel segmentation methodologies in retinal images: a survey[J]. Computer Methods and Programs in Biomedicine, 108, 407-433(2012).

    [2] Cheung C Y L, Sabanayagam C, Law A K P et al. Retinal vascular geometry and 6 year incidence and progression of diabetic retinopathy[J]. Diabetologia, 60, 1770-1781(2017).

    [3] Chan K K W, Tang F Y, Tham C C Y et al. Retinal vasculature in glaucoma: a review[J]. BMJ Open Ophthalmology, 1, e000032(2017).

    [4] Wong T Y, Klein R, Klein B E K et al. Retinal microvascular abnormalities and their relationship with hypertension, cardiovascular disease, and mortality[J]. Survey of Ophthalmology, 46, 59-80(2001).

    [5] Zhang S, Li Y P. Retinal vascular image segmentation based on improved HED network[J]. Acta Optica Sinica, 40, 0610002(2020).

    [6] Dai H, Yang Y L, Yue X et al. Denoising method of retinal OCT images based on modularized denoising autoencoder[J]. Acta Optica Sinica, 43, 0110001(2023).

    [7] Liang L M, Yin J, Wu Y Y et al. Medical image segmentation algorithm based on bilateral fusion[J]. Laser & Optoelectronics Progress, 59, 0817003(2022).

    [8] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Medical image computing and computer-assisted intervention-MICCAI 2015, 9351, 234-241(2015).

    [9] Zhou Z W, Siddiquee M M R, Tajbakhsh N et al. UNet++: a nested U-net architecture for medical image segmentation[M]. Deep learning in medical image analysis and multimodal learning for clinical decision support, 11045, 3-11(2018).

    [10] Huang H M, Lin L F, Tong R F et al. UNet 3+: a full-scale connected UNet for medical image segmentation[C], 1055-1059(2020).

    [11] Fu Z J, Li J J, Hua Z. MSA-Net: multiscale spatial attention network for medical image segmentation[J]. Alexandria Engineering Journal, 70, 453-473(2023).

    [12] Laibacher T, Weyde T, Jalali S. M2U-net: effective and efficient retinal vessel segmentation for real-world applications[C], 115-124(2019).

    [13] Sandler M, Howard A, Zhu M L et al. MobileNetV2: inverted residuals and linear bottlenecks[C], 4510-4520(2018).

    [14] Biswas R, Vasan A, Roy S S. Dilated deep neural network for segmentation of retinal blood vessels in fundus images[J]. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44, 505-518(2020).

    [15] Zhang W H, Qu S J. Retinal vessel segmentation based on multi-scale attention feature fusion network with dual-decoder structure[J]. Computer Engineering & Science, 45, 2175-2185(2023).

    [16] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

    [17] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

    [18] Hong Y D, Pan H H, Sun W C et al. Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes[EB/OL]. http:∥arxiv.org/abs/2101.06085v2

    [19] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

    [20] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Computer vision-ECCV 2018, 11211, 3-19(2018).

    [21] Owen C G, Rudnicka A R, Mullen R et al. Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) program[J]. Investigative Ophthalmology & Visual Science, 50, 2004-2010(2009).

    [22] Staal J, Abramoff M D, Niemeijer M et al. Ridge-based vessel segmentation in color images of the retina[J]. IEEE Transactions on Medical Imaging, 23, 501-509(2004).

    [23] Zhuang J T. LadderNet: multi-path networks based on U-Net for medical image segmentation[EB/OL]. http:∥arxiv.org/abs/1810.07810v4

    [24] Wu Y C, Xia Y, Song Y et al. Multiscale network followed network model for retinal vessel segmentation[M]. Medical image computing and computer assisted intervention-MICCAI 2018, 11071, 119-126(2018).

    [25] Wang B, Qiu S, He H. Dual encoding U-Net for retinal vessel segmentation[M]. Medical image computing and computer assisted intervention-MICCAI 2019, 11764, 84-92(2019).

    [26] Wu T F, Li L L, Li J B. MSCAN: multi-scale channel attention for fundus retinal vessel segmentation[C], 18-27(2020).

    [27] Sun K X, Xin Y L, Qi Y L et al. CAGU-net: category attention guidance U-net for retinal blood vessel segmentation[C], 151-155(2021).

    [28] Beeche C, Singh J P, Leader J K et al. Super U-Net: a modularized generalizable architecture[J]. Pattern Recognition, 128, 108669(2022).

    [29] Zhang T, Li J, Zhao Y et al. MC-UNet multi-module concatenation based on U-shape network for retinal blood vessels segmentation[EB/OL]. https:∥arxiv.org/abs/2204.03213v1

    [30] Sun K, Chen Y, Chao Y et al. A retinal vessel segmentation method based improved U-Net model[J]. Biomedical Signal Processing and Control, 82, 104574(2023).

    [31] Zhang H Y, Ni W H, Luo Y et al. TUnet-LBF: retinal fundus image fine segmentation model based on transformer Unet network and LBF[J]. Computers in Biology and Medicine, 159, 106937(2023).

    [32] Zhou W, Bai W Q, Ji J H et al. Dual-path multi-scale context dense aggregation network for retinal vessel segmentation[J]. Computers in Biology and Medicine, 164, 107269(2023).

    [33] Zhang Z X, Liu Q J, Wang Y H. Road extraction by deep residual U-net[J]. IEEE Geoscience and Remote Sensing Letters, 15, 749-753(2018).

    [34] Ibtehaz N, Kihara D. ACC-UNet: a completely convolutional UNet model for the 2020s[M]. International conference on medical image computing and computer-assisted intervention- MICCAI 2023, 14222, 692-702(2023).