Automated Segmentation of Liver and Liver Tumors with SwinUNETR and UNET Neural Networks on 18F-FDG PET/CT
Year: 2025
Type: article
Abstract: Objectives: F-FDG PET/CT) using SwinUNETR and residual UNET architectures, and to assess their accuracy in complex clinical cases. Methods: F-FDG-avid hepatic lesions from various primary malignancies were included. Liver segmentation was performed on non-contrast CT images using pairs of SwinUNETR and residual UNET models, and tumor segmentation was performed on masked PET images using separately trained pair of SwinUNETR and residual UNET model. Model performance was evaluated using the dice similarity coefficient (DSC), volumetric bias, and Bland-Altman analysis for metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Results: For liver segmentation, SwinUNETR achieved a median DSC of 97.59% (range: 95.41-98.93%) with a median volumetric bias of -0.94% (LoA: -3.76% to +0.50%), while residual UNET achieved a median DSC of 97.85% (range: 94.81-98.80%) with a median volumetric bias of -0.34% (LoA: -2.63% to +1.16%). For tumor segmentation, SwinUNETR achieved a median DSC of 92.62% (range: 80.75-97.46%), an MTV bias of -8.60% (LoA: -31.62% to +1.21%), and a TLG bias of -6.40% (LoA: -25.58% to +0.76%). Residual UNET achieved a median DSC of 93.07% (range: 80.74-98.18%), MTV bias of -4.33% (LoA: -24.36% to +10.12%), and TLG bias of -11.10% (LoA: -30.8% to +4.52%). Most MTV and TLG measurements were within ±10% of reference values. Conclusion: F-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.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416840839
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