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

We propose a marginal super-resolution (MSR) approach based on 2Dconvolutional neural networks (CNNs) for interpolating an anisotropic brainmagnetic resonance scan along the highly under-sampled direction, which isassumed to axial without loss of generality. Previous methods for sliceinterpolation only consider data from pairs of adjacent 2D slices. Thepossibility of fusing information from the direction orthogonal to the 2Dslices remains unexplored. Our approach performs MSR in both sagittal andcoronal directions, which provides an initial estimate for slice interpolation.The interpolated slices are then fused and refined in the axial direction forimproved consistency. Since MSR consists of only 2D operations, it is morefeasible in terms of GPU memory consumption and requires fewer training samplescompared to 3D CNNs. Our experiments demonstrate that the proposed methodoutperforms traditional linear interpolation and baseline 2D/3D CNN-basedapproaches. We conclude by showcasing the method's practical utility inestimating brain volumes from under-sampled brain MR scans through semanticsegmentation.

Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement