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.

翻译：在生物医学图像分析中,良好的分解结果可以帮助医生做出判断,并进一步改善病人的经验。在医学图像分析中,在现有的许多管道中,Unet是最受欢迎的神经网络之一,因为它通过添加编码器和解码器之间的连接来保持原始特征,从而使其在工业领域仍然广泛使用。在中间,作为主导自然语言进程任务的流行模型,变压器现在被引入计算机视觉任务,并在物体检测、图像分类和语义分解任务中看到有希望的结果。因此,变压器和Unet的结合被认为比单独使用这两种方法的效率要高。在本篇文章中,我们提出变压器-Unet,办法是在原始图像中添加变压器模块,而不是在Unet的特征图中,并相应地在Pancreas分解的CT82数据集中测试我们的网络。我们形成了一个端对端网络,获得的分解结果比我们实验中以前的许多基于Unet的算法要好。我们展示了我们的网络,并在该文件中相应地展示我们的实验结果。