Segmentation of Liver Tumor in CT Scan Using ResU-Net

Muhammad Waheed Sabir, Zia Khan, Naufal M. Saad, Danish M. Khan, Mahmoud Ahmad Al-Khasawneh, Kiran Perveen, Abdul Qayyum, Syed Saad Azhar Ali*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Segmentation of images is a common task within medical image analysis and a necessary component of medical image segmentation. The segmentation of the liver and liver tumors is an important but challenging stage in screening and diagnosing liver diseases. Although many automated techniques have been developed for liver and tumor segmentation; however, segmentation of the liver is still challenging due to the fuzzy & complex background of the liver position with other organs. As a result, creating a considerable automated liver and tumour division from CT scans is critical for identifying liver cancer. In this article, deeply dense-network ResU-Net architecture is implemented on CT scan using the 3D-IRCADb01 dataset. An essential feature of ResU-Net is the residual block and U-Net architecture, which extract additional information from the input data compared to the traditional U-Net network. Before being fed to the deep neural network, image pre-processing techniques are applied, including data augmentation, Hounsfield windowing unit, and histogram equalization. The ResU-Net network performance is evaluated using the dice similarity coefficient (DSC) metric. The ResU-Net system with residual connections outperformed state-of-the-art approaches for liver tumour identification, with a DSC value of 0.97% for organ recognition and 0.83% for segmentation methods.

Original languageEnglish
Article number8650
JournalApplied Sciences (Switzerland)
Volume12
Issue number17
DOIs
StatePublished - Sep 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Keywords

  • ResU-Net
  • deep learning
  • liver segmentation
  • medical imaging
  • tumor detection

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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