The autoPET3 challenge finds that leading AI models reach a mean Dice score of 0.66 for multitracer PET/CT lesion segmentation, with compositional generalization to unseen tracer-center pairs remaining an open problem driven by volume overestimation and case heterogeneity.
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An uncertainty-aware framework with Bayesian ensembling and epistemic uncertainty-augmented training improves lesion segmentation robustness on public multi-tracer PET/CT datasets over standard nnU-Net.
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The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization
The autoPET3 challenge finds that leading AI models reach a mean Dice score of 0.66 for multitracer PET/CT lesion segmentation, with compositional generalization to unseen tracer-center pairs remaining an open problem driven by volume overestimation and case heterogeneity.
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Improving PET/CT-Based Whole-Body Lesion Segmentation Using Prediction Uncertainty-Augmented Models
An uncertainty-aware framework with Bayesian ensembling and epistemic uncertainty-augmented training improves lesion segmentation robustness on public multi-tracer PET/CT datasets over standard nnU-Net.