English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Automated segmentation of white matter hyperintensities (WMHs) is anessential step in neuroimaging analysis of Magnetic Resonance Imaging (MRI).Fluid Attenuated Inversion Recovery (FLAIR-weighted) is an MRI contrast that isparticularly useful to visualize and quantify WMHs, a hallmark of cerebralsmall vessel disease and Alzheimer's disease (AD). Clinical MRI protocolsmigrate to a three-dimensional (3D) FLAIR-weighted acquisition to enable highspatial resolution in all three voxel dimensions. The current study details thedeployment of deep learning tools to enable automated WMH segmentation andcharacterization from 3D FLAIR-weighted images acquired as part of a nationalAD imaging initiative.  Among 441 participants (194 male, mean age: (64.91 +/- 9.32) years) from theDDI study, two in-house networks were trained and validated across fivenational collection sites. Three models were tested on a held-out subset of theinternal data from the 441 participants and an external dataset with 29 casesfrom an international collaborator. These test sets were evaluatedindependently. Five established WMH performance metrics were used forcomparison against ground truth human-in-the-loop segmentation.  Results of the three networks tested, the 3D nnU-Net had the best performancewith an average dice similarity coefficient score of 0.76 +/- 0.16, performingbetter than both the in-house developed 2.5D model and the SOTA Deep Bayesiannetwork.  With the increasing use of 3D FLAIR-weighted images in MRI protocols, ourresults suggest that WMH segmentation models can be trained on 3D data andyield WMH segmentation performance that is comparable to or better thanstate-of-the-art without the need for including T1-weighted image series.

Segmenting white matter hyperintensities on isotropic three-dimensional  Fluid Attenuated Inversion Recovery magnetic resonance images: Assessing deep  learning tools on norwegian imaging database