Opto-Electronic Engineering, Volume. 50, Issue 1, 220116(2023)

Boundary attention assisted dynamic graph convolution for retinal vascular segmentation

Jia Lv1...2,*, Zeyu Wang1 and Haocheng Liang1 |Show fewer author(s)
Author Affiliations
  • 1College of Computer and Information Sciences, Chongqing Normal University, Chongqing 401331, China
  • 2China National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China
  • show less
    References(11)

    [4] [4] Zhou Y Q, Yu H C, Shi H. Study group learning: improving retinal vessel segmentation trained with noisy labels[C]//Proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, 2021: 57–67. https://doi.org/10.1007/978-3-030-87193-2_6.

    [5] [5] Ummadi V. U-net and its variants for medical image segmentation: a short review[Z]. arXiv: 2204.08470, 2022. https://arxiv.org/abs/2204.08470v1.

    [7] [7] Zhang T, Li J, Zhao Y, et al. MC-UNet multi-module concatenation based on u-shape network for retinal blood vessels segmentation[Z]. arXiv: 2204.03213, 2022. https://arxiv.org/abs/2204.03213v1.

    [9] [9] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[Z]. arXiv: 1609.02907, 2016. https://arxiv.org/abs/1609.02907.

    [11] [11] Meng Y D, Wei M, Gao D X, et al. CNN-GCN aggregation enabled boundary regression for biomedical image segmentation[C]//Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, 2020: 352–362. https://doi.org/10.1007/978-3-030-59719-1_35.

    [13] [13] Li X, Yang Y B, Zhao Q J, et al. Spatial pyramid based graph reasoning for semantic segmentation[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 2020: 8950–8959. https://doi.org/10.1109/CVPR42600.2020.00897.

    [14] [14] Zhang Y S, Chung A C S. Deep supervision with additional labels for retinal vessel segmentation task[C]//Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, 2018: 83–91. https://doi.org/10.1007/978-3-030-00934-2_10.

    [16] [16] Yu F, Wang D Q, Shelhamer E, et al. Deep layer aggregation[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 2403–2412. https://doi.org/10.1109/CVPR.2018.00255.

    [18] [18] Chen X, Qi D L, Shen J X. Boundary-aware network for fast and high-accuracy portrait segmentation[Z]. arXiv: 1901.03814, 2019. https://arxiv.org/abs/1901.03814.

    [19] [19] Yu C Q, Wang J B, Peng C, et al. BiSeNet: bilateral segmentation network for real-time semantic segmentation[C]//Proceedings of the 15th European Conference on Computer Vision, Munich, 2018: 325–341. https://doi.org/10.1007/978-3-030-01261-8_20.

    [21] [21] Li L Z, Verma M, Nakashima Y, et al. IterNet: retinal image segmentation utilizing structural redundancy in vessel networks[C]//Proceedings of 2020 IEEE Winter Conference on Applications of Computer Vision, Snowmass, 2020: 3656–3665. https://doi.org/10.1109/WACV45572.2020.9093621.

    Tools

    Get Citation

    Copy Citation Text

    Jia Lv, Zeyu Wang, Haocheng Liang. Boundary attention assisted dynamic graph convolution for retinal vascular segmentation[J]. Opto-Electronic Engineering, 2023, 50(1): 220116

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Article

    Received: Jun. 8, 2022

    Accepted: Sep. 27, 2022

    Published Online: Feb. 27, 2023

    The Author Email: Lv Jia (lvjia@cqnu.edu.cn)

    DOI:10.12086/oee.2023.220116

    Topics