ArticleCardiovascular engineering and technology2026
Shape and Scale in Quantifying Aortic Morphology Evolution and Chronicity.
Article in Cardiovascular engineering and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The Case for Interpretable Geometry: Statistical Shape Models vs. Curvature-Based Descriptors in Aortic Disease Classification.medRxiv : the preprint server for health sciences · 2026Article
- Predicting curvature evolution on biological surfaces from clinical imaging-derived area dilation: a closed-form interpretable framework.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeDecision-making in thoracic aortic disease primarily relies on diameter thresholds that compress rich three-dimensional morphology into a single length scale. Intrinsic shape measures have been shown to complement diameter, but their utility depends critically on the observation scale imposed during quantification. We aim to identify scales at which a size-invariant shape descriptor, specifically the normalized fluctuation in total integrated Gaussian curvature
methodsWe construct a scale space of aortic surface representations from CTA images via a factorial sweep across smoothing intensity, mesh density, and mesh partitioning (coarse-graining) size. For each construction we compute
resultsA reproducible stable zone emerges in which centimeter-scale partitioning consistently maximizes the informativeness of
conclusionOptimizing scale space yields a tuned, size-invariant shape signal that is both robust and clinically interpretable. The observed association between
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