Accurate segmentation of the stroke lesions using magnetic resonance imaging
(MRI) is associated with difficulties due to the complicated anatomy of the
brain and the different properties of the lesions. This study introduces the
Neuro-TransUNet framework, which synergizes the U-Net's spatial feature
extraction with SwinUNETR's global contextual processing ability, further
enhanced by advanced feature fusion and segmentation synthesis techniques. The
comprehensive data pre-processing pipeline improves the framework's efficiency,
which involves resampling, bias correction, and data standardization, enhancing
data quality and consistency. Ablation studies confirm the significant impact
of the advanced integration of U-Net with SwinUNETR and data pre-processing
pipelines on performance and demonstrate the model's effectiveness. The
proposed Neuro-TransUNet model, trained with the ATLAS v2.0 \emph{training}
dataset, outperforms existing deep learning algorithms and establishes a new
benchmark in stroke lesion segmentation.

通过将 U-Net 的空间特征提取与 SwinUNETR 的全局上下文处理能力相结合，再进一步采用先进的特征融合和分割合成技术，提出了 Neuro-TransUNet 框架。通过全面的数据预处理管道改进了这一框架的效率，包括重采样、偏差校正和数据标准化，提高了数据质量和一致性。消融研究证实了 U-Net 与 SwinUNETR 的先进集成以及数据预处理管道对性能的重要影响，并证明了该模型的有效性。使用 ATLAS v2.0 训练数据集训练的所提出的 Neuro-TransUNet 模型在中风病变分割方面优于现有的深度学习算法，并建立了一个新的基准。