Metraipolis › archive › dc_ca71a9dcda52

Text-Guided Refinement of Multi-sequence Glioma Subregion Segmentation with a Vision-Language Foundation Model — arXiv:2608.05389

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.05389
License (as archived): CC0-1.0
Archived: 2026-10-31T10:35:53.697Z
SHA-256 of the archived text: 93f50f1276a05305d3602270fc265b1f26b841b89f3ddc7d22c712b984df19cc
Stable URL of this page: https://metraipolis.com/documento/dc_ca71a9dcda52

How to cite

Text-Guided Refinement of Multi-sequence Glioma Subregion Segmentation with a Vision-Language Foundation Model — arXiv:2608.05389 — arXiv (en), https://arxiv.org/abs/2608.05389 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ca71a9dcda52 (Metraipolis archive dc_ca71a9dcda52, sha256 93f50f1276a05305d3602270fc265b1f26b841b89f3ddc7d22c712b984df19cc)

BibTeX

@misc{dcca71a9dcda52,
  title = {Text-Guided Refinement of Multi-sequence Glioma Subregion Segmentation with a Vision-Language Foundation Model — arXiv:2608.05389},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.05389},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ca71a9dcda52; sha256 93f50f1276a05305d3602270fc265b1f26b841b89f3ddc7d22c712b984df19cc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.05389. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Background: Accurate glioma subregion delineation is important for radiotherapy planning and longitudinal monitoring, but manual contour correction is time-consuming. Models such as nnU-Net may generalize imperfectly and lack clinician-directed text correction. Purpose: We investigated adapting a three-dimensional (3D) vision-language foundation model for text-guided brain tumor segmentation refinement. Methods: We developed a lightweight VoxTell-based framework. Pretrained VoxTell generated initial masks. Oracle prompts derived from segmentation errors encoded target, action, location, imaging evidence, edit size, and preservation constraints. Frozen Qwen/VoxTell prompt embeddings were injected through trainable projections into its multiscale decoder conditioning; other weights remained frozen. Training, validation, and testing used 901, 100, and 250 BraTS-GLI cases. Cross-dataset transfer was evaluated on 100 meningioma, metastasis, pediatric tumor, and UPENN-GBM cases. Results: On the internal test set using post-contrast T1-weighted input, correct instructions improved subregion Dice similarity coefficient (DSC; enhancing tumor, edema, and necrotic/non-enhancing core) from $0.774\pm0.158$ to $0.796\pm0.137$. They outperformed blank prompts ($0.762\pm0.155$; Holm-adjusted $p<0.001$, $d_z=0.71$) and contradictory prompts ($0.770\pm0.163$; $p<0.001$, $d_z=0.48$). In cross-dataset testing, correct instructions improved DSC from $0.527\pm0.287$ to $0.550\pm0.278$ and outperformed contradictory instructions ($0.504\pm0.275$; $p<0.001$, $d_z=0.43$). Conclusion: A 3D vision-language foundation model can perform instruction-guided refinement of glioma subregion segmentations. Sensitivity to correct, blank, and contradictory prompts suggests text-dependent contour editing rather than nonspecific post-processing, supporting further evaluation as a clinician-in-the-loop tool.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-07 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.05389 — 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 2026-10-31T10:23:30.970Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).