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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| Veröffentlicht in: | Signal processing. Image communication Jg. 101; S. 116553 |
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| Sprache: | Englisch |
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Elsevier B.V
01.02.2022
Elsevier BV |
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| ISSN: | 0923-5965, 1879-2677 |
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| Abstract | 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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| AbstractList | 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. 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. |
| ArticleNumber | 116553 |
| Author | Piao, Songhao Noor, Alam AboElenein, Nagwa M. Ahmed, Pir Noman |
| Author_xml | – sequence: 1 givenname: Nagwa M. orcidid: 0000-0002-5485-3012 surname: AboElenein fullname: AboElenein, Nagwa M. organization: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China – sequence: 2 givenname: Songhao surname: Piao fullname: Piao, Songhao email: piaosh@hit.edu.cn organization: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China – sequence: 3 givenname: Alam orcidid: 0000-0002-0077-6509 surname: Noor fullname: Noor, Alam organization: CISTER Research Centre, ISEP, Politécnico do Porto, Portugal – sequence: 4 givenname: Pir Noman surname: Ahmed fullname: Ahmed, Pir Noman organization: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China |
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| Cites_doi | 10.1109/TPAMI.2015.2408351 10.1109/ICCV.2015.123 10.1186/s12880-015-0068-x 10.1109/ACCESS.2020.2983075 10.1109/CVPR.2017.683 10.1109/CVPR.2014.81 10.1109/CVPR.2018.00745 10.1016/j.media.2016.05.004 10.3389/fncom.2019.00044 10.3389/fncom.2020.00010 10.1109/CVPR.2016.90 10.1109/ACCESS.2020.2998601 10.1097/PDM.0b013e31818f071b 10.1109/ACCESS.2019.2927433 10.1016/j.compbiomed.2019.03.014 10.1016/j.media.2017.10.002 |
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| Keywords | Brain tumor segmentation Inception Residual Module Full convolutional network U-Net Attention Gate |
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| SubjectTerms | Attention Gate Brain Brain cancer Brain tumor segmentation Coders Full convolutional network Image segmentation Inception Neural networks Performance evaluation Residual Module Tumors U-Net |
| Title | MIRAU-Net: An improved neural network based on U-Net for gliomas segmentation |
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