<p>Accurate delineation of liver tumors is the key to medical image-guided precision radiotherapy. In this paper we propose and study an automatic biological target delineation algorithm based on RASUnet. We propose innovative optimization schemes at the data set level and network architecture level, and verify the effect of the improved scheme through experiments. We apply the residual idea to the Unet network architecture to form the ResUnet network, which speeds up the network convergence process. To introduce an attention gate for ResUnet, we form a RAUnet network, which can “emphasize regions of interest and suppress irrelevant regions” on the input image. Finally, we add the compression activation mechanism to form the ResSE optimization module, and improve the network to RASUnet. This method brings a weighting mechanism acting on the feature channel, which further improves the attention performance. The experimental results show that RASUnet has the best accuracy in the optimal delineation, and has high accuracy and robustness in the automatic segmentation and delineation of tumor biological targets.</p>