MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
Brain tumors are a type of disease that affects people’s health and have received extensive attention. Accurate segmentation of Magnetic Resonance Imaging (MRI) images for brain tumors is essential for effective treatment strategies. However, there is scope for enhancing the segmentation accuracy of...
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MDPI AG
2025-03-01
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| author | Ruihao Zhang Peng Yang Can Hu Bin Guo |
| author_facet | Ruihao Zhang Peng Yang Can Hu Bin Guo |
| author_sort | Ruihao Zhang |
| collection | DOAJ |
| description | Brain tumors are a type of disease that affects people’s health and have received extensive attention. Accurate segmentation of Magnetic Resonance Imaging (MRI) images for brain tumors is essential for effective treatment strategies. However, there is scope for enhancing the segmentation accuracy of established deep learning approaches, such as 3D U-Net. In pursuit of improved segmentation precision for brain tumor MRI images, we propose the MEASegNet, which incorporates multiple efficient attention mechanisms into the 3D U-Net architecture. The encoder employs Parallel Channel and Spatial Attention Block (PCSAB), the bottleneck layer leverages Channel Reduce Residual Atrous Spatial Pyramid Pooling (CRRASPP) attention, and the decoder layer incorporates Selective Large Receptive Field Block (SLRFB). Through the integration of various attention mechanisms, we enhance the capacity for detailed feature extraction, facilitate the interplay among distinct features, and ensure the retention of more comprehensive feature information. Consequently, this leads to an enhancement in the segmentation precision of 3D U-Net for brain tumor MRI images. In conclusion, our extensive experimentation on the BraTS2021 dataset yields Dice scores of 92.50%, 87.49%, and 84.16% for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET), respectively. These results indicate a marked improvement in segmentation accuracy over the conventional 3D U-Net. |
| format | Article |
| id | doaj-art-6aa62b2b939b4969ab7f8d2bf76f783b |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-6aa62b2b939b4969ab7f8d2bf76f783b2025-08-20T03:08:44ZengMDPI AGApplied Sciences2076-34172025-03-01157379110.3390/app15073791MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor ImagesRuihao Zhang0Peng Yang1Can Hu2Bin Guo3College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaSchool of Computer and Soft, Hohai University, Nanjing 211100, ChinaCollege of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaBrain tumors are a type of disease that affects people’s health and have received extensive attention. Accurate segmentation of Magnetic Resonance Imaging (MRI) images for brain tumors is essential for effective treatment strategies. However, there is scope for enhancing the segmentation accuracy of established deep learning approaches, such as 3D U-Net. In pursuit of improved segmentation precision for brain tumor MRI images, we propose the MEASegNet, which incorporates multiple efficient attention mechanisms into the 3D U-Net architecture. The encoder employs Parallel Channel and Spatial Attention Block (PCSAB), the bottleneck layer leverages Channel Reduce Residual Atrous Spatial Pyramid Pooling (CRRASPP) attention, and the decoder layer incorporates Selective Large Receptive Field Block (SLRFB). Through the integration of various attention mechanisms, we enhance the capacity for detailed feature extraction, facilitate the interplay among distinct features, and ensure the retention of more comprehensive feature information. Consequently, this leads to an enhancement in the segmentation precision of 3D U-Net for brain tumor MRI images. In conclusion, our extensive experimentation on the BraTS2021 dataset yields Dice scores of 92.50%, 87.49%, and 84.16% for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET), respectively. These results indicate a marked improvement in segmentation accuracy over the conventional 3D U-Net.https://www.mdpi.com/2076-3417/15/7/37913D U-Netbrain tumor segmentationattention mechanism |
| spellingShingle | Ruihao Zhang Peng Yang Can Hu Bin Guo MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images Applied Sciences 3D U-Net brain tumor segmentation attention mechanism |
| title | MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images |
| title_full | MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images |
| title_fullStr | MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images |
| title_full_unstemmed | MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images |
| title_short | MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images |
| title_sort | measegnet 3d u net with multiple efficient attention for segmentation of brain tumor images |
| topic | 3D U-Net brain tumor segmentation attention mechanism |
| url | https://www.mdpi.com/2076-3417/15/7/3791 |
| work_keys_str_mv | AT ruihaozhang measegnet3dunetwithmultipleefficientattentionforsegmentationofbraintumorimages AT pengyang measegnet3dunetwithmultipleefficientattentionforsegmentationofbraintumorimages AT canhu measegnet3dunetwithmultipleefficientattentionforsegmentationofbraintumorimages AT binguo measegnet3dunetwithmultipleefficientattentionforsegmentationofbraintumorimages |