An artificial intelligence-assisted framework for fast and automatic radiofrequency ablation planning of liver tumors in CT images

To develop and validate an artificial intelligence (AI)-assisted framework for fast and automatic radiofrequency ablation (RFA) planning of liver tumors from CT images. This framework consisted of three steps. First, the abdominal multi-organs related to RFA planning were automatically segmented from CT images using a modified nnU-Net with class-weighted loss function. Then, utilizing the segmented liver as a location prior, the liver tumors and hepatic vessels were further segmented by a sensitivity-enhanced segmentation network. Finally, a clinically acceptable RFA plan was generated by a fully automatic planning method based on the segmented organs and tumors. Experiments were conducted on two public segmentation datasets and patients from two different centers to evaluate the proposed framework. The proposed abdominal multi-organ segmentation model achieved an average dice of 87.7 ± 8.0 ± 11.2 ± 10.2 ± 12.9 ± 14.9