MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans
The accurate segmentation of brain tumors from medical images is critical for diagnosis and treatment planning. However, traditional segmentation methods struggle with complex tumor shapes and inconsistent image quality which leads to suboptimal results. To address this challenge, we propose multipl...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/12/2/140 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850080265820438528 |
|---|---|
| author | Yu Lyu Xiaolin Tian |
| author_facet | Yu Lyu Xiaolin Tian |
| author_sort | Yu Lyu |
| collection | DOAJ |
| description | The accurate segmentation of brain tumors from medical images is critical for diagnosis and treatment planning. However, traditional segmentation methods struggle with complex tumor shapes and inconsistent image quality which leads to suboptimal results. To address this challenge, we propose multiple tasking Wasserstein Generative Adversarial Network U-shape Network++ (MWG-UNet++) to brain tumor segmentation by integrating a U-Net architecture enhanced with transformer layers which combined with Wasserstein Generative Adversarial Networks (WGAN) for data augmentation. The proposed model called Residual Attention U-shaped Network (RAUNet) for brain tumor segmentation leverages the robust feature extraction capabilities of U-Net and the global context awareness provided by transformers to improve segmentation accuracy. Incorporating WGAN for data augmentation addresses the challenge of limited medical imaging datasets to generate high-quality synthetic images that enhance model training and generalization. Our comprehensive evaluation demonstrates that this hybrid model significantly improves segmentation performance. The RAUNet outperforms compared approaches by capturing long-range dependencies and considering spatial variations. The use of WGANs augments the dataset for resulting in robust training and improved resilience to overfitting. The average evaluation metric for brain tumor segmentation is 0.8965 which outperformed the compared methods. |
| format | Article |
| id | doaj-art-264fbb22522d4fcd960cf5c9272b7c68 |
| institution | DOAJ |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-264fbb22522d4fcd960cf5c9272b7c682025-08-20T02:44:59ZengMDPI AGBioengineering2306-53542025-01-0112214010.3390/bioengineering12020140MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI ScansYu Lyu0Xiaolin Tian1School of Computer Science and Engineering, Faculty of Information Technology, Macau University of Science and Technology, Macao 999078, ChinaSchool of Computer Science and Engineering, Faculty of Information Technology, Macau University of Science and Technology, Macao 999078, ChinaThe accurate segmentation of brain tumors from medical images is critical for diagnosis and treatment planning. However, traditional segmentation methods struggle with complex tumor shapes and inconsistent image quality which leads to suboptimal results. To address this challenge, we propose multiple tasking Wasserstein Generative Adversarial Network U-shape Network++ (MWG-UNet++) to brain tumor segmentation by integrating a U-Net architecture enhanced with transformer layers which combined with Wasserstein Generative Adversarial Networks (WGAN) for data augmentation. The proposed model called Residual Attention U-shaped Network (RAUNet) for brain tumor segmentation leverages the robust feature extraction capabilities of U-Net and the global context awareness provided by transformers to improve segmentation accuracy. Incorporating WGAN for data augmentation addresses the challenge of limited medical imaging datasets to generate high-quality synthetic images that enhance model training and generalization. Our comprehensive evaluation demonstrates that this hybrid model significantly improves segmentation performance. The RAUNet outperforms compared approaches by capturing long-range dependencies and considering spatial variations. The use of WGANs augments the dataset for resulting in robust training and improved resilience to overfitting. The average evaluation metric for brain tumor segmentation is 0.8965 which outperformed the compared methods.https://www.mdpi.com/2306-5354/12/2/140WGANbrain tumor segmentationMRIU-Netattention mechanism |
| spellingShingle | Yu Lyu Xiaolin Tian MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans Bioengineering WGAN brain tumor segmentation MRI U-Net attention mechanism |
| title | MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans |
| title_full | MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans |
| title_fullStr | MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans |
| title_full_unstemmed | MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans |
| title_short | MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans |
| title_sort | mwg unet hybrid transformer u net model for brain tumor segmentation in mri scans |
| topic | WGAN brain tumor segmentation MRI U-Net attention mechanism |
| url | https://www.mdpi.com/2306-5354/12/2/140 |
| work_keys_str_mv | AT yulyu mwgunethybridtransformerunetmodelforbraintumorsegmentationinmriscans AT xiaolintian mwgunethybridtransformerunetmodelforbraintumorsegmentationinmriscans |