Self-attention U-Net (SAU-Net): An attention-driven U-Net framework for precise brain tumor segmentation using multimodal magnetic resonance imaging

• PavanRaj

  Author

• Dr. Pavankumar Naik

  Author

Objectives: The primary goal is to address the challenges in brain tumor segmentation (BraTS), such as limited accuracy
and high computational costs, by developing a more precise and efficient segmentation technique. The study aims to
improve the diagnosis and treatment planning of brain tumors by enabling clinicians to accurately localize and assess
tumor regions from multimodal magnetic resonance imaging (MRI) scans.
Methods: We proposed self-attention U-Net (SAU-Net), a novel model that integrated self-attention mechanisms with
the U-Net convolutional architecture. This design allowed the model to preserve spatial context while selectively con
centrating on pertinent features, thereby enhancing tumor (ET) boundary delineation and overall segmentation accuracy.
Extensive experiments were conducted on the BraTS 2018 and BraTS 2020 datasets using thorough cross-validation and
testing protocols. The performance of SAU-Net was evaluated and compared against other attention-based U-Net mod
els, including adaptive attention U-Net, multi-head attention U-Net, and group query attention U-Net.
Results: On the BraTS 2018 dataset, SAU-Net achieved Dice scores of 98.16% (whole tumor (WT)), 98.87% (tumor
core (TC)), and 98.23% (ET), with an average Dice score of 98.23%. For the BraTS 2020 dataset, the model recorded
Dice scores of 98.99% (WT), 98.70% (TC), and 99.18% (ET), with an average Dice score of 98.62%. In addition to super
ior segmentation performance, the model demonstrated reduced computational complexity in both training and predic
tion times, along with optimized memory usage.
Conclusion: SAU-Net is a highly effective and computationally efficient model for BraTS. Its superior performance, as
evidenced by the high Dice scores on two benchmark datasets, combined with its reduced computational requirements,
underscores its potential for practical and impactful clinical applications.