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Anatomy-Aware 3D Mesh Refinement of Pericardium Segmentations on Computed Tomography

Research Medical/Healthcare AI

Merged summary

TL;DR - A GPU-accelerated 3D mesh-refinement framework uses anatomical context and geometric constraints to improve pericardium segmentation in cardiac CT. As a model-agnostic post-processing step, it is especially promising for weak or out-of-domain initial segmentations.

  • Iteratively moves mesh vertices using a 3D vector field derived from anatomical rules.
  • Improves volumetric, surface, and anatomical metrics across high-resolution and sparsely annotated datasets.
  • Delivers larger gains on weaker initial segmentations.
  • Can potentially extend to segmentation of other anatomical structures.

Sources (1)

Anatomy-Aware 3D Mesh Refinement of Pericardium Segmentations on Computed Tomography

arXiv cs.CV Andreas W. Aspe, Jonas Jalili Loft, Michael Huy Cuong Pham, Andreas Ohrt Johansen, Jørgen Tobias Kühl, Klaus Fuglsang Kofoed, Kristine Aavild Sørensen, Rasmus R. Paulsen, Josefine Vilsbøll Sundgaard 2026-07-21 arXiv:2607.19210

TL;DR - A GPU-accelerated 3D mesh-refinement framework uses anatomical context and geometric constraints to improve pericardium segmentation in cardiac CT. As a model-agnostic post-processing step, it is especially promising for weak or out-of-domain initial segmentations.

  • Iteratively moves mesh vertices using a 3D vector field derived from anatomical rules.
  • Improves volumetric, surface, and anatomical metrics across high-resolution and sparsely annotated datasets.
  • Delivers larger gains on weaker initial segmentations.
  • Can potentially extend to segmentation of other anatomical structures.
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