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126014

Automated Deep System for Joint Liver and Tumor Segmentation Using Majority Voting

Article

Last updated: 04 Jan 2025

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Subjects

-

Tags

Biomedical Engineering

Abstract

In this paper, a system based on deep learning and majority voting is proposed for joint segmentation of the liver and hepatic tumors. The proposed system is composed of three steps. First, deep learning is utilized to extract deep features that describe the Computed Tomography (CT) images as well as cancerous nodules, using three different Convolutional Neural Networks (CNNs), i.e., VGG16-Segnet, Encoder-Decoder (ED)-Alexnet, and Resnet18. Second, a classification step using the extracted deep learning features is performed for each investigated network. To produce the final liver and hepatic tumor segmentation, the last step applies a majority voting technique to fuse the three utilized CNN outputs. To test the performance of the proposed system, the MICCAI LITS challenge database is used, composed of 130 CT volumes with a total of 16,917 cross-section images. The proposed system achieves Dice Similarity Coefficients (DSCs) of 94% and 76% for liver and lesion segmentations, respectively. Comparison with the related methods confirms the promise of the proposed system for joint liver and tumor segmentations

DOI

10.21608/bfemu.2020.126014

Keywords

Liver, hepatic, Aided, Diagnostic, Deep learning

Authors

First Name

Ahmed

Last Name

Elnakib

MiddleName

-

Affiliation

ECE department, Faculty of Engineering, Mansoura University, Egypt

Email

nakib@mans.edu.eg

City

Masnoura

Orcid

0000-0001-6084-3622

First Name

N.

Last Name

Elmenabawy

MiddleName

-

Affiliation

Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, 35516 Mansoura City, Egypt

Email

nermeena@yahoo.com

City

-

Orcid

-

First Name

H.

Last Name

S.Moustafa

MiddleName

-

Affiliation

Associate Professor of Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, 35516 Mansoura City, Egypt

Email

hossam_moustafa@hotmail.com

City

Mansoura

Orcid

-

Volume

45

Article Issue

4

Related Issue

17795

Issue Date

2020-12-01

Receive Date

2020-08-13

Publish Date

2020-11-26

Page Start

30

Page End

36

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 45_Issue 4_Pages 30-36.pdf

PDF . 731.3kB

Link

https://bfemu.journals.ekb.eg/article_126014.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=126014

Order

22

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

Automated Deep System for Joint Liver and Tumor Segmentation Using Majority Voting

Details

Type

Article

Created At

22 Jan 2023