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Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings

Research Article

Training U-Net with Proportional Image Division for Retinal Structure Segmentation

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  • @INPROCEEDINGS{10.1007/978-3-031-60665-6_9,
        author={Pedro Victor de Abreu Fonseca and Alexandre Carvalho Ara\^{u}jo and Jo\"{a}o Dallyson S. de Almeida and Geraldo Braz J\^{u}nior and Arist\^{o}fanes Correa Silva and Rodrigo de Melo Souza Veras},
        title={Training U-Net with Proportional Image Division for Retinal Structure Segmentation},
        proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings},
        proceedings_a={MOBIHEALTH},
        year={2024},
        month={6},
        keywords={Segmentation Retinal fundus Proportional Image Division U-Net},
        doi={10.1007/978-3-031-60665-6_9}
    }
    
  • Pedro Victor de Abreu Fonseca
    Alexandre Carvalho Araújo
    João Dallyson S. de Almeida
    Geraldo Braz Júnior
    Aristófanes Correa Silva
    Rodrigo de Melo Souza Veras
    Year: 2024
    Training U-Net with Proportional Image Division for Retinal Structure Segmentation
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-031-60665-6_9
Pedro Victor de Abreu Fonseca1, Alexandre Carvalho Araújo1, João Dallyson S. de Almeida1,*, Geraldo Braz Júnior1, Aristófanes Correa Silva1, Rodrigo de Melo Souza Veras
  • 1: Federal University of Maranhão - UFMA
*Contact email: jdallyson@nca.ufma.br

Abstract

Cup and optic disc segmentation has become one of the main objects of study in the field of creating and improving machine learning-oriented models due to the importance of vision for human beings and the ability to assist physicians in diagnosing ocular problems. Within this context, this study presents a new method based on the proportional division of images concerning features extracted from the sample set. These samples go through a pre-processing step involving image resizing before going to deep feature extraction and K-means clustering, thus dividing the set for validation and training. Soon after, the amount of samples is increased through data augmentation before going on to the U-Net training. The proposed method has been evaluated on the public RIM-ONE and DRISHTI-GS datasets, and presented promising results in the segmentation of both structures, with emphasis on obtaining the value of 92.2% of Dice for the segmentation of the optic cup in the DRISHTI-GS test dataset and 95.9% of Dice for the optic disc in the RIM-ONE.

Keywords
Segmentation Retinal fundus Proportional Image Division U-Net
Published
2024-06-28
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-60665-6_9
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