  Medical image segmentation is one of the most fundamental tasks concerning
medical information analysis. Various solutions have been proposed so far,
including many deep learning-based techniques, such as U-Net, FC-DenseNet, etc.
However, high-precision medical image segmentation remains a highly challenging
task due to the existence of inherent magnification and distortion in medical
images as well as the presence of lesions with similar density to normal
tissues. In this paper, we propose TFCNs (Transformers for Fully Convolutional
denseNets) to tackle the problem by introducing ResLinear-Transformer
(RL-Transformer) and Convolutional Linear Attention Block (CLAB) to
FC-DenseNet. TFCNs is not only able to utilize more latent information from the
CT images for feature extraction, but also can capture and disseminate semantic
features and filter non-semantic features more effectively through the CLAB
module. Our experimental results show that TFCNs can achieve state-of-the-art
performance with dice scores of 83.72\% on the Synapse dataset. In addition, we
evaluate the robustness of TFCNs for lesion area effects on the COVID-19 public
datasets. The Python code will be made publicly available on
https://github.com/HUANGLIZI/TFCNs.
