Transformer-Unet: Raw Image Processing with Unet

TL;DR

This paper introduces Transformer-Unet, a novel neural network combining transformers with Unet for improved raw image segmentation, demonstrating superior results in pancreas segmentation on CT82 datasets.

Contribution

The paper proposes integrating transformer modules directly into raw images within Unet, enhancing segmentation performance over existing Unet-based methods.

Findings

Outperforms previous Unet-based algorithms in pancreas segmentation

Effective end-to-end architecture for raw image processing

Shows promising results in medical image segmentation

Abstract

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.