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。

神经-TransUNet：使用Transformer进行MRI中的中风病灶分割