Abstract：Objective The segmentation of the liver and liver tumor is an important step in the radiotherapy planning for liver cancer.
In this paper, we proposed a novel automatic segmentation model to realize the accurate segmentation of liver and liver tumors.
Methods The residual module and the Swim Transformer module were added to a 3D UNet deep neural network, and a new Res-
Swim-UNet segmentation model combining convolution and Transformer was proposed. We compared the performance of proposed
and previous methods on the LiTS public dataset, and verified the generalization ability of Res-Swim-UNet model on a local
dataset. Results The dice similarity coefficient (DSC) and volumetric overlap error (VOE) of the liver segmentation results of the
Res-Swim-UNet model on the LiTS public dataset were 0.957 and 0.522, respectively. Compared with the UNet model, the DSC
increased by 1.6%, and the VOE decreased by 1.3%. The DSC and VOE of the liver tumor segmentation results were 0.672 and
0.617, respectively, which was 13.5% higher than the UNet model in DSC, and 5.9% lower in VOE. The DSC and VOE of the liver
segmentation results on the local dataset were 0.895 and 0.552, respectively, and the DSC and VOE of the liver tumor segmentation
results were 0.589 and 0.706, respectively. Conclusion The Res-Swim-UNet model proposed in this paper can effectively improve
the segmentation effect of liver and liver tumors in CT images, and the model still has high segmentation accuracy when transferred
to local dataset. The model could be used to improve the efficiency of the target delineation for physician.
戴振晖，简婉薇，朱琳，张白霖，靳怀志，杨耕，谭翔，王学涛. 基于3D UNet结合Transformer的肝脏及肝肿瘤自动分割[J]. 中国医疗设备, 2023, 38(1): 42-47.
DAI Zhenhui, JIAN Wanwei, ZHU Lin, ZHANG Bailin, JIN Huaizhi, YANG Geng, TAN Xiang, WANG Xuetao. Automatic Segmentation of the Liver and Liver Tumor Based on
3D UNet Combined with Transformer. China Medical Devices, 2023, 38(1): 42-47.
