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

Deep learning approaches for diffusion MRI have so far focused primarily onvoxel-based segmentation of lesions or white-matter fiber tracts. A drawback ofrepresenting tracts as volumetric labels, rather than sets of streamlines, isthat it precludes point-wise analyses of microstructural or geometric featuresalong a tract. Traditional tractography pipelines, which do allow suchanalyses, can benefit from detailed whole-brain segmentations to guide tractreconstruction. Here, we introduce fast, deep learning-based segmentation of170 anatomical regions directly on diffusion-weighted MR images, removing thedependency of conventional segmentation methods on T 1-weighted images and slowpre-processing pipelines. Working natively in diffusion space avoids non-lineardistortions and registration errors across modalities, as well as interpolationartifacts. We demonstrate consistent segmentation results between 0 .70 and 0.87 Dice depending on the tissue type. We investigate various combinations ofdiffusion-derived inputs and show generalization across different numbers ofgradient directions. Finally, integrating our approach to provide anatomicalpriors for tractography pipelines, such as TRACULA, removes hours ofpre-processing time and permits processing even in the absence of high-qualityT 1-weighted scans, without degrading the quality of the resulting tractestimates.

Learning Anatomical Segmentations for Tractography from Diffusion MRI