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

Segmentation of brain tumors and their subregions remains a challenging taskdue to their weak features and deformable shapes. In this paper, three patterns(cross-skip, skip-1 and skip-2) of distributed dense connections (DDCs) areproposed to enhance feature reuse and propagation of CNNs by constructingtunnels between key layers of the network. For better detecting and segmentingbrain tumors from multi-modal 3D MR images, CNN-based models embedded with DDCs(DDU-Nets) are trained efficiently from pixel to pixel with a limited number ofparameters. Postprocessing is then applied to refine the segmentation resultsby reducing the false-positive samples. The proposed method is evaluated on theBraTS 2019 dataset with results demonstrating the effectiveness of the DDU-Netswhile requiring less computational cost.

DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation