@inproceedings{870af5b753734e24a08a8d2d6646e76b,
title = "Liver Segmentation in Computed Tomography Images Using Transformers, Inception Module and U-Net",
abstract = "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.",
keywords = "attention, liver segmentation, transformers, U-Net",
author = "Zossou, {Vincent Beni Sena} and Allodji, {Rodrigue S.} and Olivier Biaou and Ezin, {Eugene C.}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 10th International Conference on Applied System Innovation, ICASI 2024 ; Conference date: 17-04-2024 Through 21-04-2024",
year = "2024",
month = jan,
day = "1",
doi = "10.1109/ICASI60819.2024.10547912",
language = "English",
series = "Proceedings of the 2024 10th International Conference on Applied System Innovation, ICASI 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "199--201",
editor = "Shoou-Jinn Chang and Sheng-Joue Young and Lam, {Artde Donald Kin-Tak} and Liang-Wen Ji and Prior, {Stephen D.}",
booktitle = "Proceedings of the 2024 10th International Conference on Applied System Innovation, ICASI 2024",
}