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Midline shift is an important clinical indicator of the severity of hemorrhagic stroke and holds significance in a physician's clinical diagnosis. Segmentation-based methods for midline shift assessment are prevalent in the field, but the full utilization of global information within network remains a challenge. In addition, empirical integration with clinical knowledge is also essential. In this study, we developed a two-stage method for automatic midline shift assessment. Firstly, in the midline identification step, we proposed a Dual-Path U-Transformer to segment the brain midline. The Dual-Path U-Transformer can better capitalize on global information by integrating self-attention mechanism, while still retaining the characteristics of U-Net in making full use of local information and combining high and low dimensional features. In the second stage, according to clinical knowledge learned from clinical expert, we calculated the maximum shift distance for assessment of brain midline shift, determining whether each case has surgical indication. In the experiments process, we use 5-fold cross validation to train and validate the proposed model. Compared with traditional U-Net based method and transformer-based method, the proposed Dual-Path U-Transformer based method performed the best HD and Dice performance on our inhouse dataset. And the experiment results confirmed that the Dual-Path U-Transformer based exhibited excellent accuracy in the second stage of midline shift assessment.
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