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    Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images

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    Author
    Salem, Nema cc
    Naveed, Khuram cc
    Akram, Awais
    Afaq, Amir
    Madni, Hussain
    Khan, Mohammad cc
    din, Mui
    Raza, Mohsin
    Date
    2021-12-31
    
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    Abstract
    In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.
    Department
    Electrical and Computer Engineering
    Publisher
    PLOS ONE
    Journal title
    Plos-one
    DOI
    https://doi.org/10.1371/journal.pone.0261698
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1371/journal.pone.0261698
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