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Spatial Atlas-Informed Loss Ensembles for Whole-Body PSMA PET/CT Metastasis Segmentation — arXiv:2610.40834

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40834
License (as archived): CC0-1.0
Archived: 2026-11-02T05:03:31.800Z
SHA-256 of the archived text: da4772cfdc9cd619e9a24c0cb9d3a33e80a5ce9443a60c9855eb92f43125f621
Stable URL of this page: https://metraipolis.com/documento/dc_670ade9900bb

How to cite

Spatial Atlas-Informed Loss Ensembles for Whole-Body PSMA PET/CT Metastasis Segmentation — arXiv:2610.40834 — arXiv (en), https://arxiv.org/abs/2610.40834 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_670ade9900bb (Metraipolis archive dc_670ade9900bb, sha256 da4772cfdc9cd619e9a24c0cb9d3a33e80a5ce9443a60c9855eb92f43125f621)

BibTeX

@misc{dc670ade9900bb,
  title = {Spatial Atlas-Informed Loss Ensembles for Whole-Body PSMA PET/CT Metastasis Segmentation — arXiv:2610.40834},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40834},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_670ade9900bb; sha256 da4772cfdc9cd619e9a24c0cb9d3a33e80a5ce9443a60c9855eb92f43125f621},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40834. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Existing whole-body PSMA PET/CT prostate metastasis segmentation methods generally rely on images alone, without explicitly incorporating where metastases tend to occur. We investigate whether a population metastasis atlas can improve lesion detection when integrated into model training. We constructed voxel-wise mean and standard deviation maps from 375 expert-annotated training scans and used them to weight the cross-entropy loss of an nnU-Net. The additional penalties for missed lesions and false positives depend on spatial atlas statistics and diminish as predictions become confidently correct. Seven configurations were compared on validation data; three were combined by majority voting into SAILS (Spatial Atlas-Informed Loss enSemble). We compared SAILS with a baseline nnU-Net and inference-time atlas fusion on AutoPET~III (110 disease-positive cases) and Deep-PSMA (100 external cases). No atlas or registration is needed at inference. Results: On AutoPET~III, SAILS improved whole-body Dice (0.531 vs. 0.520; p=0.006), reduced missed-lesion volume (9.5 vs. 12.1 cc/case; p<0.01), and increased pooled lesion sensitivity (0.857 vs. 0.843; p=0.004). On Deep-PSMA, SAILS detected 126 additional reference lesions (sensitivity 0.769 vs. 0.751; p<0.001), without a significant whole-body Dice change (-0.009; p=0.36). High-risk bone Dice improved in both cohorts (0.576 vs. 0.552 and 0.667 vs. 0.653; both p<0.01). False-positive volume increased by 2.3 and 18.2 cc/case, respectively (p<0.001). Atlas-weighted training improves lesion detection, especially in high-risk bone, without test-time atlas registration, but increases false positives.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.40834 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T04:49:13.221Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).