Automated Segmentation of Liver and Liver Tumors with SwinUNETR and UNET Neural Networks on 18F-FDG PET/CT

Both SwinUNETR and Residual UNET achieved excellent liver segmentation accuracy and clinically acceptable tumor segmentation performance on ¹⁸F-FDG PET/CT, with SwinUNETR showing slightly better performance in liver volumetric measurements. These open-source models could be integrated into clinical workflows to automate segmentation tasks, facilitate treatment planning for liver-directed therapies, and support reproducible quantitative imaging analyses.