Deep Joint Segmentation of Liver and Cancerous Nodules from Ct Images

Nermeen A. Elmenabawy, Ahmed Elnakib, Hossam El Din Moustafa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

A framework is proposed for joint liver and cancerous nodule segmentation from abdomen computed tomography (CT) images. The proposed framework consists of three main units. First, a preprocessing unit is used to enhance the image contrast. Second, two different deep convolutional-deconvolutional neural networks (CDNN), namely, Alexnet and Resnet18 models, are investigated to extract the features of liver images. Finally, a pixel wise classification unit is performed to provide the final segmentation maps of the liver and tumors. Results on the challenging MICCAI'2017 liver tumor segmentation (LITS) database, using Alexnet model and 4-fold cross-validation, achieve a Dice similarity coefficient of 90.4% for liver segmentation and of 62.4% for lesion segmentation. Comparative results with related techniques for joint liver and tumor segmentations show the effectiveness of the proposed framework.

Original languageEnglish (US)
Title of host publicationProceedings of 2020 37th National Radio Science Conference, NRSC 2020
EditorsRowayda Sadek, Mohamed Ashour
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages296-301
Number of pages6
ISBN (Electronic)9781728168197
DOIs
StatePublished - Sep 8 2020
Event37th National Radio Science Conference, NRSC 2020 - Cairo, Egypt
Duration: Sep 8 2020Sep 10 2020

Publication series

NameNational Radio Science Conference, NRSC, Proceedings
Volume2020-September

Conference

Conference37th National Radio Science Conference, NRSC 2020
Country/TerritoryEgypt
CityCairo
Period9/8/209/10/20

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Condensed Matter Physics
  • Electronic, Optical and Magnetic Materials

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