Neuro-TransUNet: Segmentation of stroke lesion in MRI using transformers
This addresses the problem of stroke lesion segmentation for medical imaging, but it is incremental as it combines existing methods.
The study tackled accurate stroke lesion segmentation in MRI by introducing Neuro-TransUNet, which synergizes U-Net and SwinUNETR with advanced feature fusion, and it outperformed existing deep learning algorithms to set a new benchmark.
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.