ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2026
AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization.
Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Evaluating Automated Tools for Lesion Detection onRadiology. Imaging cancer · 2025Trial
- End-to-End PET/CT Interpretation and Quantification with an LLM-Orchestrated AI Agent: A Real-World Pilot Study.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Benchmarking Deep Learning for NSCLC PET/CT Segmentation on a Histologically Confirmed Vietnamese Dataset: Validation and Generalization.Journal of imaging · 2026Article
- A Whole-Body PSMA-PET/CT dataset with manually annotated tumor lesions.Scientific data · 2026Article
- AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Development and validation of pan-cancer lesion segmentation AI-model for whole-body 18F-FDG PET/CT in diverse clinical cohorts.Computers in biology and medicine · 2025Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- Whole-body tumor segmentation from FDG-PET/CT: Leveraging a segmentation prior from tissue-wise projections.Heliyon · 2025Article
- Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges.Digital healthReview
Corrections and comments
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Authors and funding
34 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article reports the results of the second iteration of the autoPET challenge on automated lesion segmentation in whole-body PET/CT, held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention in 2023. In contrast to the first autoPET challenge, which served as a proof of concept, this study investigates whether machine learning-based segmentation models trained on data from a single source can maintain performance across clinically relevant variations in PET/CT data, reflecting the demands of real-world deployment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.