Brain tumor segmentation is an active research area due to the difficulty in
delineating highly complex shaped and textured tumors as well as the failure of
the commonly used U-Net architectures. The combination of different neural
architectures is among the mainstream research recently, particularly the
combination of U-Net with Transformers because of their innate attention
mechanism and pixel-wise labeling. Different from previous efforts, this paper
proposes a novel network architecture that integrates Transformers into a
self-adaptive U-Net to draw out 3D volumetric contexts with reasonable
computational costs. We further add a residual connection to prevent
degradation in information flow and explore ensemble methods, as the evaluated
models have edges on different cases and sub-regions. On the BraTS 2021 dataset
(3D), our model achieves 87.6% mean Dice score and outperforms the
state-of-the-art methods, demonstrating the potential for combining multiple
architectures to optimize brain tumor segmentation.

提出了一种将 Transformers 集成到自适应 U-Net 中的新型网络架构，以合理的计算成本提取出三维体积上下文，并探索集成方法，证明了组合多个架构以优化脑肿瘤分割的潜力。