| Title: | Identifying Morphological Indicators of Aging With Neural Networks on Large-Scale Whole-Body MRI |
| Journal: | IEEE Trans Med Imaging |
| Published: | 1 May 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/31675324/ |
| DOI: | https://doi.org/10.1109/TMI.2019.2950092 |
Publication 3073
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
A wealth of information is contained in images obtained by whole-body magnetic resonance imaging (MRI). Studying the link between the imaged anatomy and properties known from outside sources has the potential to give new insights into the underlying factors that manifest themselves in individual human morphology. In this work we investigate the expression of age-related changes in the whole-body image. A large dataset of about 32,000 subjects scanned from neck to knee and aged 44-82 years from the UK Biobank study was used for a machine-based analysis. We trained a convolutional neural network based on the VGG16 architecture to predict the age of a given subject based on image data from these scans.1 Author
- Langner T et al.
1 Application
| Application ID | Title |
|---|---|
| 14237 | Application of Imiomics to whole-body MRI data for creation and application of a human imaging atlas |
Enabling scientific discoveries that improve human health