  Medical image segmentation have drawn massive attention as it is important in
biomedical image analysis. Good segmentation results can assist doctors with
their judgement and further improve patients' experience. Among many available
pipelines in medical image analysis, Unet is one of the most popular neural
networks as it keeps raw features by adding concatenation between encoder and
decoder, which makes it still widely used in industrial field. In the mean
time, as a popular model which dominates natural language process tasks,
transformer is now introduced to computer vision tasks and have seen promising
results in object detection, image classification and semantic segmentation
tasks. Therefore, the combination of transformer and Unet is supposed to be
more efficient than both methods working individually. In this article, we
propose Transformer-Unet by adding transformer modules in raw images instead of
feature maps in Unet and test our network in CT82 datasets for Pancreas
segmentation accordingly. We form an end-to-end network and gain segmentation
results better than many previous Unet based algorithms in our experiment. We
demonstrate our network and show our experimental results in this paper
accordingly.
