Although the medical image segmentation model SCTransNet based on convolutional neural network and Transformer performs well, SCTransNet has the problems of large number of parameters and computation. When SCTransNet processes the 3D shadow data, the 3D convolution operation in the CNN encoder and the decoder, and the Transformer model needs a large number of parameters to learn when operating on the 3D feature image, leading to the increase in the number of model parameters and the rising computational cost. Considering the problem of large number of parameters and calculation, this paper proposed the lightweight model LiteSCTransNet to effectively reduce the parameters and computation of the model while ensuring the segmentation accuracy. The encoder module in the Transformer model is refined using the channel separation idea, replacing the conventional convolution operation using deeply separable convolution. Experiments show that the calculated lightweight method reduced the number of parameters and calculations by 74.43% and 75.21%, respectively. Moreover, the Dice coefficient of liver and liver tumor segmentation in the LiTS-2017 dataset are 96.91% and 70.13%, respectively. The lightweight model has high operation efficiency and segmentation accuracy.