Title: | Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners |
Journal: | NeuroImage |
Published: | 4 Jul 2020 |
Pubmed: | https://pubmed.ncbi.nlm.nih.gov/32634595/ |
DOI: | https://doi.org/10.1016/j.neuroimage.2020.117127 |
Title: | Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners |
Journal: | NeuroImage |
Published: | 4 Jul 2020 |
Pubmed: | https://pubmed.ncbi.nlm.nih.gov/32634595/ |
DOI: | https://doi.org/10.1016/j.neuroimage.2020.117127 |
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.
•We present Neuroharmony, a harmonization tool for images from unseen scanners.•We developed Neuroharmony using a total of 15,026 sMRI images.•The tool was able to reduce scanner-related bias from unseen scans.•Neuroharmony represents a significant step towards imaging-based clinical tools.•Neuroharmony is available at https://github.com/garciadias/Neuroharmony. We present Neuroharmony, a harmonization tool for images from unseen scanners. We developed Neuroharmony using a total of 15,026 sMRI images. The tool was able to reduce scanner-related bias from unseen scans. Neuroharmony represents a significant step towards imaging-based clinical tools. Neuroharmony is available at https://github.com/garciadias/Neuroharmony.</p>
Application ID | Title |
---|---|
40323 | Using deep learning technology to make individualised inferences in brain-based disorders |
Enabling scientific discoveries that improve human health