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EA-Net: Edge Attention Network for Brain Tumour Segmentation in MRI
Meng Fansheng1, Duan Xingguang1,2, Song Xinya3
1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.
Summary
This study introduces an Edge Attention Network for precise brain tumor segmentation, achieving high accuracy on the BraTS2021 dataset and demonstrating strong generalization for improved clinical applications.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Biology
Background:
- Accurate brain tumor segmentation is vital for clinical diagnosis and treatment planning.
- Challenges include diverse tumor scales, ambiguous boundaries, and irregular shapes.
Purpose of the Study:
- To develop an advanced deep learning model for improved brain tumor segmentation.
- To enhance segmentation accuracy and robustness in medical imaging.
Main Methods:
- Proposed a novel Edge Attention Network incorporating a Multi-Scale Context Fusion Module.
- Integrated an Edge Segmentation Module to extract and utilize tumor boundaries for refined segmentation.
- Employed spatial attention mechanisms to focus on critical segmentation details, especially at tumor edges.
Main Results:
- Achieved high performance on the BraTS2021 dataset with Dice coefficients of 90.37% for Tumor Core (TC) and 88.91% for Whole Tumor (WT).
- Demonstrated strong cross-dataset generalization on BTM-PVS, yielding 75.20% TC and 74.20% WT.
- The model effectively refines segmentation details, particularly at tumor edges.
Conclusions:
- The Edge Attention Network shows superior segmentation accuracy and robust generalization capabilities.
- The method holds significant clinical potential for brain tumor analysis.
- Offers novel insights for advancing medical image segmentation techniques.
