AI models struggle with unseen PET/CT tracer combinations despite segmentation gains

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

影响 Highlights limitations in compositional generalization for medical image segmentation models, indicating areas for future research and development.

排序理由 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]

在 arXiv cs.CV 阅读 →

• [18F]/[68Ga]-PSMA
• arXiv
• autoPET3 Challenge
• LMU University Hospital Munich
• MICCAI 2024
• nnU-Net
• PET/CT
• University Hospital Tübingen
• [18F]-FDG

• 论文
• 其他

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