The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization
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| Format: | Preprint |
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2026
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| author | Dexl, Jakob Jeblick, Katharina Mittermeier, Andreas Schachtner, Balthasar Stüber, Anna Theresa Topalis, Johanna Rokuss, Maximilian Isensee, Fabian Maier-Hein, Klaus H. Kalisch, Hamza Kleesiek, Jens Seibold, Constantin M. Alasmawi, Hussain Chan, Lap Yan Lennon Yuan, Yixuan Jaus, Alexander Stiefelhagen, Rainer Choudja, Pauline Ornela Megne Nikolaou, Konstantin La Fougère, Christian Gatidis, Sergios Fabritius, Matthias P. Heimer, Maurice Abaci, Gizem Sundar, Lalith Kumar Shiyam Werner, Rudolf A. Ricke, Jens Cyran, Clemens C. Küstner, Thomas Ingrisch, Michael |
| author_facet | Dexl, Jakob Jeblick, Katharina Mittermeier, Andreas Schachtner, Balthasar Stüber, Anna Theresa Topalis, Johanna Rokuss, Maximilian Isensee, Fabian Maier-Hein, Klaus H. Kalisch, Hamza Kleesiek, Jens Seibold, Constantin M. Alasmawi, Hussain Chan, Lap Yan Lennon Yuan, Yixuan Jaus, Alexander Stiefelhagen, Rainer Choudja, Pauline Ornela Megne Nikolaou, Konstantin La Fougère, Christian Gatidis, Sergios Fabritius, Matthias P. Heimer, Maurice Abaci, Gizem Sundar, Lalith Kumar Shiyam Werner, Rudolf A. Ricke, Jens Cyran, Clemens C. Küstner, Thomas Ingrisch, Michael |
| contents | We report the design and results of the third autoPET challenge (MICCAI 2024), which benchmarked automated lesion segmentation in whole-body PET/CT under a compositional generalization setting. Training data comprised 1,014 [18F]-FDG PET/CT studies from the University Hospital Tübingen and 597 [18F]/[68Ga]-PSMA PET/CT studies from the LMU University Hospital Munich, constituting the largest publicly available annotated PSMA PET/CT dataset to date. The held-out test set of 200 studies covered four tracer-center combinations, two of which represented unseen compositional pairings. A complementary data-centric award category isolated the contribution of data handling strategies by restricting participants to a fixed baseline model. Seventeen teams submitted 27 algorithms, predominantly nnU-Net-based 3D networks with PET/CT channel concatenation. The top-ranked algorithm achieved a mean DSC of 0.66, FNV of 3.18 mL, and FPV of 2.78 mL across all four test conditions, improving DSC by 8% and reducing the false-negative volume by 5 mL relative to the provided baseline. Ranking was stable across bootstrap resampling and alternative ranking schemes for the top tier. Beyond the benchmark, we provide an in-depth analysis of segmentation performance at the patient and lesion level. Three main conclusions can be drawn: (1) in-domain multitracer PET/CT segmentation is sufficient and probably approaching reader agreement; (2) compositional generalization to unseen tracer-center combinations remains an open problem mainly driven by systematic volume overestimation; (3) heterogeneity and case difficulty drive performance variation substantially more than the choice of algorithm among top-ranked teams. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05775 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization Dexl, Jakob Jeblick, Katharina Mittermeier, Andreas Schachtner, Balthasar Stüber, Anna Theresa Topalis, Johanna Rokuss, Maximilian Isensee, Fabian Maier-Hein, Klaus H. Kalisch, Hamza Kleesiek, Jens Seibold, Constantin M. Alasmawi, Hussain Chan, Lap Yan Lennon Yuan, Yixuan Jaus, Alexander Stiefelhagen, Rainer Choudja, Pauline Ornela Megne Nikolaou, Konstantin La Fougère, Christian Gatidis, Sergios Fabritius, Matthias P. Heimer, Maurice Abaci, Gizem Sundar, Lalith Kumar Shiyam Werner, Rudolf A. Ricke, Jens Cyran, Clemens C. Küstner, Thomas Ingrisch, Michael Computer Vision and Pattern Recognition Artificial Intelligence We report the design and results of the third autoPET challenge (MICCAI 2024), which benchmarked automated lesion segmentation in whole-body PET/CT under a compositional generalization setting. Training data comprised 1,014 [18F]-FDG PET/CT studies from the University Hospital Tübingen and 597 [18F]/[68Ga]-PSMA PET/CT studies from the LMU University Hospital Munich, constituting the largest publicly available annotated PSMA PET/CT dataset to date. The held-out test set of 200 studies covered four tracer-center combinations, two of which represented unseen compositional pairings. A complementary data-centric award category isolated the contribution of data handling strategies by restricting participants to a fixed baseline model. Seventeen teams submitted 27 algorithms, predominantly nnU-Net-based 3D networks with PET/CT channel concatenation. The top-ranked algorithm achieved a mean DSC of 0.66, FNV of 3.18 mL, and FPV of 2.78 mL across all four test conditions, improving DSC by 8% and reducing the false-negative volume by 5 mL relative to the provided baseline. Ranking was stable across bootstrap resampling and alternative ranking schemes for the top tier. Beyond the benchmark, we provide an in-depth analysis of segmentation performance at the patient and lesion level. Three main conclusions can be drawn: (1) in-domain multitracer PET/CT segmentation is sufficient and probably approaching reader agreement; (2) compositional generalization to unseen tracer-center combinations remains an open problem mainly driven by systematic volume overestimation; (3) heterogeneity and case difficulty drive performance variation substantially more than the choice of algorithm among top-ranked teams. |
| title | The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.05775 |