# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 7 Conclusions  
This paper introduces MEASegNet, a U-shaped network tailored for brain tumor MRI segmentation. MEASegNet integrates three novel attention modules: PCSAB, CRRASPP, and SLRFB. SLRFB, positioned in the decoder, deeply integrates global and local features to enhance feature extraction robustness. CRRASPP, located in the bottleneck layer, captures and effectively fuses multiscale information, thereby strengthening feature interactions. PCSAB further refines these features, improving segmentation performance on the basis of enhanced multiscale information. The effectiveness of these attention modules was empirically validated. Compared with 11 state-of-the-art networks, including 3D U-Net, U-Net variants with attention mechanisms, and Transformer-based models, MEASegNet achieved superior Dice scores for WT (92.50%), TC (87.49%), ET (84.16%), and the average (88.05%). Additionally, ablation studies on the combination of PCSAB, CRRASPP, and SLRFB demonstrated that integrating diverse attention mechanisms at different stages of the 3D U-Net architecture significantly boosts its performance. These results highlight the fact that designing specialized attention modules for distinct regions and integrating them effectively can markedly enhance the brain tumor MRI segmentation capabilities of 3D U-Net. In the network's bottleneck layer, capturing multiscale features is advantageous, but it is not always the case that more scales lead to better performance. Effective fusion of multiscale features and enhanced interactions between feature information are also crucial in this layer. Moreover, our medical case visualization studies confirm the feasibility of using MEASegNet for segmenting brain tumor MRI images. We believe that our method provides a robust theoretical foundation for future research and offers valuable insights for developing treatment plans for brain tumors.  
Conceptualization, B.G.; methodology, B.G. and R.Z.; software, R.Z. and P.Y.; data curation, C.H.; writing--original draft, R.Z.; writing--review and editing, B.G. and R.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Datasets released to the public were analyzed in this study. The BraTS2021 dataset can be found through the following link: [[https://www.med.upenn.edu/cbica/brats2021/#Data2](https://www.med.upenn.edu/cbica/brats2021/#Data2)]([https://www.med.upenn.edu/cbica/brats2021/#Data2](https://www.med.upenn.edu/cbica/brats2021/#Data2)) (accessed on 14 March 2025). The BraTS2019 dataset can be found through the following link: [[https://www.med.upenn.edu/cbica/brats2019/](https://www.med.upenn.edu/cbica/brats2019/)]([https://www.med.upenn.edu/cbica/brats2019/](https://www.med.upenn.edu/cbica/brats2019/)) (accessed on 14 March 2025). The BraTS2020 dataset can be found through the following link: [[https://www.med.upenn.edu/cbica/brats2020/](https://www.med.upenn.edu/cbica/brats2020/)]([https://www.med.upenn.edu/cbica/brats2020/](https://www.med.upenn.edu/cbica/brats2020/)) (accessed on 14 March 2025).  
The authors declare no conflicts of interest.