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VoxelSage: Tool-Augmented 3D CT Analysis and Simulator-Shielded Sequential Resection Planning for Liver Tumors — arXiv:2609.37648

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.37648
License (as archived): CC0-1.0
Archived: 2026-10-07T18:41:25.133Z
SHA-256 of the archived text: af531e63bba3f493fb8bc7df93b68e86e2520da7e946fdd82259fd3b59a8df7b
Stable URL of this page: https://metraipolis.com/documento/dc_7d415e404bdd

How to cite

VoxelSage: Tool-Augmented 3D CT Analysis and Simulator-Shielded Sequential Resection Planning for Liver Tumors — arXiv:2609.37648 — arXiv (en), https://arxiv.org/abs/2609.37648 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7d415e404bdd (Metraipolis archive dc_7d415e404bdd, sha256 af531e63bba3f493fb8bc7df93b68e86e2520da7e946fdd82259fd3b59a8df7b)

BibTeX

@misc{dc7d415e404bdd,
  title = {VoxelSage: Tool-Augmented 3D CT Analysis and Simulator-Shielded Sequential Resection Planning for Liver Tumors — arXiv:2609.37648},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.37648},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7d415e404bdd; sha256 af531e63bba3f493fb8bc7df93b68e86e2520da7e946fdd82259fd3b59a8df7b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.37648. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Preoperative liver-tumor assessment requires segmentation, physical-space measurement, visual evidence, and resection planning from the same three-dimensional CT volume. Existing tools often handle these steps separately, while language models cannot reliably compute physical measurements from CT. To provide an integrated workflow, we present VoxelSage, a multi-modal system for two- and three-dimensional visualization, liver-tumor analysis, and preoperative resection planning. Its dual-port architecture separates language-model orchestration from image computation: Port A interprets requests and selects skills, while Port B applies them to CT volumes and segmentation masks and returns structured results. Keeping physical measurements in Port B prevents the LLM from computing them directly and reduces the risk of fabricated numerical results. Eight built-in skills support quantitative analysis, visual evidence generation, three-dimensional reconstruction, segmentation refinement, and sequential resection planning; user-defined skills can extend these functions. For sequence planning, a behavior-cloned neural ranker orders candidate resection targets, while a simulator-based shield checks them against predefined constraints. Across 256 unseen simulator scenes, this approach reduced mean simulated time from 34.274 to 33.388 min (0.886 min, 2.59%) and mean simulated blood loss from 300.847 to 183.852 mL (116.995 mL, 38.89%) relative to a deterministic baseline. These results demonstrate system integration and simulator-level performance, not clinical efficacy or safety. The public implementation is available at this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-30 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Image and Video Processing (eess.IV).
Pagina del lavoro: https://arxiv.org/abs/2609.37648 — 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 2026-10-07T18:04:24.892Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).