IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i22p4288-d974372.html
   My bibliography  Save this article

An End-to-End Real-Time Lightweight Network for the Joint Segmentation of Optic Disc and Optic Cup on Fundus Images

Author

Listed:
  • Zhijie Liu

    (School of Computer Science and Engineering, Jishou University, Jishou 416000, China
    School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China)

  • Yuanqiong Chen

    (School of Computer Science and Engineering, Jishou University, Jishou 416000, China
    School of Computer Science and Engineering, Central South University, Changsha 410083, China)

  • Xiaohua Xiang

    (Department of Computer Science, Xiangxi National Vocational and Technical College, Jishou 416000, China)

  • Zhan Li

    (Department of Computer Science, Swansea University, Swansea SA1 8EN, UK)

  • Bolin Liao

    (School of Computer Science and Engineering, Jishou University, Jishou 416000, China)

  • Jianfeng Li

    (School of Computer Science and Engineering, Jishou University, Jishou 416000, China
    School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China)

Abstract

Glaucoma is the second-most-blinding eye disease in the world and accurate segmentation of the optic disc (OD) and optic cup (OC) is essential for the diagnosis of glaucoma. To solve the problems of poor real-time performance, high algorithm complexity, and large memory consumption of fundus segmentation algorithms, a lightweight segmentation algorithm, GlauNet, based on convolutional neural networks, is proposed. The algorithm designs an efficient feature-extraction network and proposes a multiscale boundary fusion (MBF) module, which greatly improves the segmentation efficiency of the algorithm while ensuring segmentation accuracy. Experiments show that the algorithm achieves Dice scores of 0.9701/0.8959, 0.9650/0.8621, and 0.9594/0.8795 on three publicly available datasets—Drishti-GS, RIM-ONE-r3, and REFUGE-train—for both the optic disc and the optic cup. The number of model parameters is only 0.8 M, and it only takes 13 ms to infer an 800 × 800 fundus image on a GTX 3070 GPU.

Suggested Citation

  • Zhijie Liu & Yuanqiong Chen & Xiaohua Xiang & Zhan Li & Bolin Liao & Jianfeng Li, 2022. "An End-to-End Real-Time Lightweight Network for the Joint Segmentation of Optic Disc and Optic Cup on Fundus Images," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4288-:d:974372
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/22/4288/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/22/4288/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4288-:d:974372. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.