MRI Brain Tumor Segmentation Using Deep Learning.

Authors

Shimaa Nassar, Electronics and Communications Engineering (ECE) Department, Faculty of Engineering, Mansoura University, EgyptFollow
Mohamed Mohamed, Professor of Electronics and Communications Engineering (ECE) Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.Follow
Ahmed Elnakib, ECE department, Faculty of Engineering, Mansoura University, EgyptFollow

Corresponding Author

Elnakib, Ahmed

Subject Area

Electronics and Communication Engineering

Article Type

Original Study

Abstract

This work presents a method for classification and segmentation of brain tumors based on deep learning analysis of brain contrast T1 (T1c) MR images. To achieve this goal, three different deep learning networks are investigated i.e., U-Net, VGG16-Segnet, and DeepLabv3+ models. In addition, the integration of the 3D narrow-band information of the MRI volumes is imported to the input of the Convolutional Neural Network (CNN) to describe more accurately the tumor anatomy. Experimentations are performed on the MICCAI’2018 High Grade Glioma (HGG) subset of the Brain Tumor Segmentation (BraTS) Challenge, composed of 210 brain T1c MRI volumes, each of 155 cross-sections. Among the three investigated CNNs, DeepLabv3+ network achieves the highest Dice Similarity Coefficients (DSC) of 91.2%, 92.5%, 94.6% for the segmentation of the Enhancing Tumor (ET), the Tumor Core (TC), and the Whole Tumor (WT), respectively. Comparison with the related work confirms the advantages of the proposed system.

Keywords

Tumor Segmentation; Deep learning; Brain MRI

Recommended Citation

Nassar, Shimaa; Mohamed, Mohamed; and Elnakib, Ahmed (2021) "MRI Brain Tumor Segmentation Using Deep Learning.," Mansoura Engineering Journal: Vol. 45 : Iss. 4 , Article 25.
Available at: https://doi.org/10.21608/bfemu.2021.139470