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All Journal International Journal of Electrical and Computer Engineering
Nermeen Elmenabawy
Mansoura university
Computer Science & IT Electrical & Electronics Engineering

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Deep segmentation of the liver and the hepatic tumors from abdomen tomography images Nermeen Elmenabawy; Mervat El-Seddek; Hossam El-Din Moustafa; Ahmed Elnakib
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp303-310

Abstract

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.
Co-Authors Ahmed Elnakib Hossam El-Din Moustafa Mervat El-Seddek