The autoPET3 challenge, held in conjunction with MICCAI 2024, focused on automated lesion segmentation in whole-body PET/CT scans, specifically testing compositional generalization. The challenge utilized a large dataset comprising over 1,600 PET/CT studies from two major hospitals, including the largest publicly available annotated PSMA PET/CT dataset. Seventeen teams developed algorithms, primarily based on nnU-Net, with the top performer achieving a mean Dice Similarity Coefficient (DSC) of 0.66. The results indicated that while in-domain segmentation is nearing human agreement, generalization to unseen tracer-center combinations remains a significant challenge, with algorithm choice having less impact than data heterogeneity among top performers. AI
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IMPACT Highlights limitations in compositional generalization for medical image segmentation models, indicating areas for future research and development.
RANK_REASON This is a research paper detailing a challenge and its results in automated medical image segmentation. [lever_c_demoted from research: ic=1 ai=1.0]