Abstract: In view of the challenges encountered in CT image processing of intracranial hemorrhage,such as the variability of the spatial position,shape and size of the hemorrhage region and the difficulty in determining the boundary due to the similar intensity value of the surrounding tissue,an improved TransUNet image segmentation model(ESC-TransUNet) is proposed.Firstly,an explicit visual center(EVC) is added to the model before upsampling,which can capture the correlation degree of far-distance pixels in the image and retain the detailed information of local corner regions in the input image,which is helpful to effectively extract the features of the bleeding region.Secondly,a shuffle attention(SA) mechanism is introduced in the encoder stage,which effectively learns the small differences between the bleeding area and the background,thus improving the accuracy of the segmentation task.Finally,CBM2 structure is used in the decoder stage to promote more effective information transmission and enhance the generalization ability and accuracy of the model.Numerous experiments have been conducted on Physionet(PHY),a publicly available dataset on intracranial hemorrhage.The results show that the proposed method outperforms the other nine main segmentation methods and achieves better performance in the task of intracranial hemorrhage CT image segmentation.

Key words: Deep learning, CT images, Intracranial hemorrhage segmentation, Shuffle attention mechanism, Explicit visual center