Self-attention U-Net (SAU-Net): An attention-driven U-Net framework for precise brain tumor segmentation using...

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.

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 concentrating 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 models, including adaptive attention U-Net, multi-head attention U-Net, and group query attention U-Net.

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 superior segmentation performance, the model demonstrated reduced computational complexity in both training and prediction times, along with optimized memory usage.

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.