MIRAU-Net: An improved neural network based on U-Net for gliomas segmentation

Gliomas are the largest prevalent and destructive of brain tumors and have crucial parts for the diagnosing and treating of MRI brain tumors during segmentation using computerized methods. Recently, U-Net architecture has achieved impressive brain tumor segmentation, but this role remains challengin...

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Bibliographic Details
Published in:Signal processing. Image communication Vol. 101; p. 116553
Main Authors: AboElenein, Nagwa M., Piao, Songhao, Noor, Alam, Ahmed, Pir Noman
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.02.2022
Elsevier BV
Subjects:
Attention Gate
Brain
Brain cancer
Brain tumor segmentation
Coders
Full convolutional network
Image segmentation
Inception
Neural networks
Performance evaluation
Residual Module
Tumors
U-Net
Brain tumor segmentation
Inception
Residual Module
Full convolutional network
U-Net
Attention Gate
ISSN:0923-5965, 1879-2677
Online Access:Get full text
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Summary:Gliomas are the largest prevalent and destructive of brain tumors and have crucial parts for the diagnosing and treating of MRI brain tumors during segmentation using computerized methods. Recently, U-Net architecture has achieved impressive brain tumor segmentation, but this role remains challenging due to the differing severity and appearance of gliomas. Therefore, we proposed a novel encoder–decoder architecture called Multi Inception Residual Attention U-Net (MIRAU-Net) in this work. It integrates residual inception modules with attention gates into U-Net to further enhance brain tumor segmentation performance. Encoder–decoder is connected in this architecture through Inception Residual pathways to decrease the distance between their maps of features. We use the weight cross-entropy and generalized Dice (GDL) with focal Tversky loss functions to resolve the class imbalance problem. The evaluation performance of MIRAU-Net checked with Brats 2019 and obtained mean dice similarities of 0.885 for the whole tumor, 0.879 for the core area, and 0.818 for the enhancement tumor. Experiment results reveal that the suggested MIRAU-Net beats its baselines and provides better efficiency than recent techniques for brain tumor segmentation. •A novel approach is proposed for brain tumor segmentation enhancement.•The proposed network combines Residual-Inception modules and Attention module with U-Net architecture.•The encoder and decoder are connected via a sequence of Inception-Res paths.•Apply the proposed network to Brats 2019.
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ISSN:0923-5965
1879-2677
DOI:10.1016/j.image.2021.116553