MRI Brain Tumor Segmentation Using Deep Learning.

Nassar, Shimaa E.; Mohamed, Mohamed Abd El-Azim; Elnakib, Ahmed

doi:10.21608/bfemu.2021.139470

MRI Brain Tumor Segmentation Using Deep Learning.

Document Type : Research Studies

Authors

¹ Electronics and Communications Engineering (ECE) Department, Faculty of Engineering, Mansoura University, Egypt

² Professor of Electronics and Communications Engineering (ECE) Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.

³ ECE department, Faculty of Engineering, Mansoura University, Egypt

10.21608/bfemu.2021.139470

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.

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