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Tensor-based Brain Surface Modeling and Analysis — arXiv:2609.03302

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.03302
License (as archived): CC0-1.0
Archived: 2026-10-17T02:56:13.095Z
SHA-256 of the archived text: f9fe5547231845cf82802710afc136a9c188179fe513dfc3f3f265fb84388ee0
Stable URL of this page: https://metraipolis.com/documento/dc_ef9182a77ded

How to cite

Tensor-based Brain Surface Modeling and Analysis — arXiv:2609.03302 — arXiv (en), https://arxiv.org/abs/2609.03302 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ef9182a77ded (Metraipolis archive dc_ef9182a77ded, sha256 f9fe5547231845cf82802710afc136a9c188179fe513dfc3f3f265fb84388ee0)

BibTeX

@misc{dcef9182a77ded,
  title = {Tensor-based Brain Surface Modeling and Analysis — arXiv:2609.03302},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.03302},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ef9182a77ded; sha256 f9fe5547231845cf82802710afc136a9c188179fe513dfc3f3f265fb84388ee0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.03302. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We present a unified computational approach to tensor-based morphometry in detecting the brain surface shape differences between two clinical groups based on magnetic resonance images. Our approach is novel in a sense that we combined surface modeling, surface data smoothing and statistical analysis in a coherent unified mathematical framework. The cerebral cortex has the topology of a 2D highly convoluted sheet. Between two different clinical groups, the local surface area and curvature of the cortex may differ. It is highly likely that such surface shape differences are not uniform over the whole cortex. By computing how such surface metrics differ, the regions of the most rapid structural differences can be localized. To increase the signal to noise ratio, diffusion smoothing based on the explicit estimation of Laplace-Beltrami operator has been developed and applied to the surface metrics. As an illustration, we demonstrate how this new tensor-based surface morphometry can be applied in localizing the cortical regions of the gray matter tissue growth and loss in the brain images longitudinally collected in the group of children.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-04 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Neurons and Cognition (q-bio.NC).
Pagina del lavoro: https://arxiv.org/abs/2609.03302 — 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 2026-10-17T02:27:18.946Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-04?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).