Deep segmentation of the liver and the hepatic tumors from abdomen tomography images

Nermeen Elmenabawy, Mervat El-Seddek, Hossam El-Din Moustafa, Ahmed Elnakib

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two output-classified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation.

Original languageEnglish (US)
Pages (from-to)303-310
Number of pages8
JournalInternational Journal of Electrical and Computer Engineering
Issue number1
StatePublished - Feb 2022

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Electrical and Electronic Engineering

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