BoneDat, a database of standardized bone morphology for in silico analyses
Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články, dataset
Grantová podpora
24-10862S
Grantová Agentura České Republiky (Grant Agency of the Czech Republic)
24-10862S
Grantová Agentura České Republiky (Grant Agency of the Czech Republic)
PubMed
40541988
PubMed Central
PMC12181331
DOI
10.1038/s41597-025-05161-y
PII: 10.1038/s41597-025-05161-y
Knihovny.cz E-zdroje
- MeSH
- dospělí MeSH
- faktografické databáze * MeSH
- kosti a kostní tkáň * anatomie a histologie diagnostické zobrazování MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- počítačová rentgenová tomografie MeSH
- počítačová simulace MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- dataset MeSH
In silico analysis is key to understanding bone structure-function relationships in orthopedics and evolutionary biology, but its potential is limited by a lack of standardized, high-quality human bone morphology datasets. This absence hinders research reproducibility and the development of reliable computational models. To overcome this, BoneDat has been developed. It is a comprehensive database containing standardized bone morphology data from 278 clinical lumbopelvic CT scans (pelvis and lower spine). The dataset includes individuals aged 16 to 91, balanced by sex across ten age groups. BoneDat provides curated segmentation masks, normalized bone geometry (volumetric meshes), and reference morphology templates organized by sex and age. By offering standardized reference geometry and enabling shape normalization, BoneDat enhances the repeatability and credibility of computational models. It also allows for integrating other open datasets, supporting the training and benchmarking of deep learning models and accelerating their path to clinical use.
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