TPUAR-net: two parallel U-net with asymmetric residual-based deep convolutional neural network for brain tumor segmentation

Mahmoud Khaled Abd-Ellah, Ashraf A.M. Khalaf, Ali Ismail Awad, Hesham F.A. Hamed

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

10 Citations (Scopus)

Abstract

The utilization of different types of brain images has been expanding, which makes manually examining each image a labor-intensive task. This study introduces a brain tumor segmentation method that uses two parallel U-Net with an asymmetric residual-based deep convolutional neural network (TPUAR-Net). The proposed method is customized to segment high and low grade glioblastomas identified from magnetic resonance imaging (MRI) data. Varieties of these tumors can appear anywhere in the brain and may have practically any shape, contrast, or size. Thus, this study used deep learning techniques based on adaptive, high-efficiency neural networks in the proposed model structure. In this paper, several high-performance models based on convolutional neural networks (CNNs) have been examined. The proposed TPUAR-Net capitalizes on different levels of global and local features in the upper and lower paths of the proposed model structure. In addition, the proposed method is configured to use the skip connection between layers and residual units to accelerate the training and testing processes. The TPUAR-Net model provides promising segmentation accuracy using MRI images from the BRATS 2017 database, while its parallelized architecture considerably improves the execution speed. The results obtained in terms of Dice, sensitivity, and specificity metrics demonstrate that TPUAR-Net outperforms other methods and achieves the state-of-the-art performance for brain tumor segmentation.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings
EditorsFakhri Karray, Alfred Yu, Aurélio Campilho
PublisherSpringer Verlag
Pages106-116
Number of pages11
ISBN (Print)9783030272715
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event16th International Conference on Image Analysis and Recognition, ICIAR 2019 - Waterloo, Canada
Duration: Aug 27 2019Aug 29 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11663 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Image Analysis and Recognition, ICIAR 2019
Country/TerritoryCanada
CityWaterloo
Period8/27/198/29/19

Keywords

  • Brain tumor segmentation
  • Computer-aided diagnosis
  • Convolutional neural networks
  • Deep learning
  • MRI images
  • Parallel U-Net
  • TPUAR-Net

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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