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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

Eff-Unet for Trachea Segmentation on CT Scans

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  • @INPROCEEDINGS{10.1007/978-3-031-60665-6_10,
        author={Arthur Guilherme Santos Fernandes and Geraldo Braz Junior and Jo\"{a}o Ot\^{a}vio Bandeira Diniz and Marcos Melo Ferreira and Jos\^{e} Ribamar Durand Rodrigues Junior and Mackele Lourrane Jurema Da Silva and Lucas Ara\^{u}jo Gon\`{e}alves},
        title={Eff-Unet for Trachea Segmentation on CT Scans},
        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={Deep Learning Semantic Segmentation Radiotherapy Fully Convolutional Neural Networks Organs at Risk},
        doi={10.1007/978-3-031-60665-6_10}
    }
    
  • Arthur Guilherme Santos Fernandes
    Geraldo Braz Junior
    João Otávio Bandeira Diniz
    Marcos Melo Ferreira
    José Ribamar Durand Rodrigues Junior
    Mackele Lourrane Jurema Da Silva
    Lucas Araújo Gonçalves
    Year: 2024
    Eff-Unet for Trachea Segmentation on CT Scans
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-031-60665-6_10
Arthur Guilherme Santos Fernandes1,*, Geraldo Braz Junior1, João Otávio Bandeira Diniz1, Marcos Melo Ferreira1, José Ribamar Durand Rodrigues Junior1, Mackele Lourrane Jurema Da Silva1, Lucas Araújo Gonçalves1
  • 1: Applied Computing Group, Federal University of Maranhão, Av. Portugueses 1996
*Contact email: arthurgsf@nca.ufma.br

Abstract

Organ at Risk segmentation has an important role in the meticulous planning of radiotherapy for cancer treatment. Its primary objective is to safeguard the surrounding healthy tissues while precisely directing radiation to target cancer cells. Currently, this task needs manual intervention by physicians, a process that can be time-consuming and susceptible to errors. Consequently, the integration of automatic segmentation methods offers the potential to accelerate the delineation of organs during radiotherapy planning. In this study, we applied Eff-Unet, a fully convolutional neural network model, and trained it to perform the semantic segmentation of trachea in computed tomography images. This approach yielded a noteworthy 78.9% dice score, underscoring its capability to enhance the efficiency and precision of organ segmentation during the radiotherapy planning process.

Keywords
Deep Learning Semantic Segmentation Radiotherapy Fully Convolutional Neural Networks Organs at Risk
Published
2024-06-28
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-60665-6_10
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