Liver Segmentation in Computed Tomography Images Using Transformers, Inception Module and U-Net

Vincent Beni Sena Zossou, Rodrigue S. Allodji, Olivier Biaou, Eugene C. Ezin

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    Résumé

    Hepatic surgery requires the segmentation of the liver from computed tomography (CT) images. Fully automated approaches are required since manual segmentation requires much time and labor. This paper proposes a deep learning model with the basic architecture U-Net to segment the liver, including a transformer and an inception modules. The Dice coefficient on the test phase was 84.8% and the Jaccard coefficient was 63.6% on 332 CT images. This work shows the usefulness of the transformers in the liver segmentation.

    langue originaleAnglais
    titreProceedings of the 2024 10th International Conference on Applied System Innovation, ICASI 2024
    rédacteurs en chefShoou-Jinn Chang, Sheng-Joue Young, Artde Donald Kin-Tak Lam, Liang-Wen Ji, Stephen D. Prior
    EditeurInstitute of Electrical and Electronics Engineers Inc.
    Pages199-201
    Nombre de pages3
    ISBN (Electronique)9798350394924
    Les DOIs
    étatPublié - 1 janv. 2024
    Evénement10th International Conference on Applied System Innovation, ICASI 2024 - Kyoto, Japon
    Durée: 17 avr. 202421 avr. 2024

    Série de publications

    NomProceedings of the 2024 10th International Conference on Applied System Innovation, ICASI 2024

    Une conférence

    Une conférence10th International Conference on Applied System Innovation, ICASI 2024
    Pays/TerritoireJapon
    La villeKyoto
    période17/04/2421/04/24

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