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/

Zendy Plus

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

Zendy Tools

Zendy Open

Accurately segmenting temporal frames of cine magnetic resonance imaging(MRI) is a crucial step in various real-time MRI guided cardiac interventions.To achieve fast and accurate visual assistance, there are strict requirementson the maximum latency and minimum throughput of the segmentation framework.State-of-the-art neural networks on this task are mostly hand-crafted tosatisfy these constraints while achieving high accuracy. On the other hand,while existing literature have demonstrated the power of neural architecturesearch (NAS) in automatically identifying the best neural architectures forvarious medical applications, they are mostly guided by accuracy, sometimeswith computation complexity, and the importance of real-time constraints areoverlooked. A major challenge is that such constraints are non-differentiableand are thus not compatible with the widely used differentiable NAS frameworks.In this paper, we present a strategy that directly handles real-timeconstraints in a differentiable NAS framework named RT-DNAS. Experiments onextended 2017 MICCAI ACDC dataset show that compared with state-of-the-artmanually and automatically designed architectures, RT-DNAS is able to identifyones with better accuracy while satisfying the real-time constraints.

RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation