ArticleMolecular imaging and radionuclide therapy2026
Automated Segmentation of Liver and Liver Tumors with SwinUNETR and UNET Neural Networks on
Article in Molecular imaging and radionuclide therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: To develop and evaluate automated segmentation models for the liver and hepatic tumors on Methods: In this single-center retrospective study, 100 patients (48 males, 52 females; mean age 61±14 years) with 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: Both SwinUNETR and Residual UNET achieved excellent liver segmentation accuracy and clinically acceptable tumor segmentation performance on
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