ResTransUNet: A hybrid CNN-transformer approach for liver and tumor segmentation in CT images.

Journal: Computers in biology and medicine
PMID: 40157314

Abstract

BACKGROUND AND OBJECTIVE: Accurate medical tumor segmentation is critical for early diagnosis and treatment planning, significantly improving patient outcomes. This study aims to enhance liver and tumor segmentation from CT and liver images by developing a novel model, ResTransUNet, which combines convolutional and transformer blocks to improve segmentation accuracy.

Authors

  • Asmaa Sabet Anwar
    Department of Computer Engineering, Faculty of Engineering, May University, Cairo, Egypt. Electronic address: asmaa.anwar2066@ci.menofia.edu.eg.
  • Khaled Amin
    Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shebin Elkom, Egypt.
  • Mohiy M Hadhoud
    Information Technology Department, Faculty of Computers and Information, Minufiya University, Shebeen El-Kom, Egypt.
  • Mina Ibrahim
    Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shebin El-kom 32511, Menoufia, Egypt.

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