Abstract: By improving the Swin Transformer coding and decoding network,  combined with the skip-linking and depth supervision mechanisms, we proposd a new image region segmentation method  of  neonatal brain based on self-attention mechanism of shifted windows to  address the issues of low signal-to-noise ratio and poor tissue contrast in segmentation of nuclear magnetic resonance imaging (MRI) images of the neonatal brain. The method could achieve accurate segmentation of multifunctional regions of the neonatal brain images after preprocessing the MRI images, and further improve the segmentation accuracy by using the maximum connected domain algorithm. The experimental results on the dHCP dataset show that the method is superior to existing methods, providing potential possibilities for early detection and intervention of neonatal brain injury.

Key words:  , brain image region segmentation, Swin Transformer coding and decoding network, neonatal MRI, self-attention, shifted window