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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Knee osteoarthritis (OA) is the most common osteoarthritis and a leadingcause of disability. Cartilage defects are regarded as major manifestations ofknee OA, which are visible by magnetic resonance imaging (MRI). Thus earlydetection and assessment for knee cartilage defects are important forprotecting patients from knee OA. In this way, many attempts have been made onknee cartilage defect assessment by applying convolutional neural networks(CNNs) to knee MRI. However, the physiologic characteristics of the cartilagemay hinder such efforts: the cartilage is a thin curved layer, implying thatonly a small portion of voxels in knee MRI can contribute to the cartilagedefect assessment; heterogeneous scanning protocols further challenge thefeasibility of the CNNs in clinical practice; the CNN-based knee cartilageevaluation results lack interpretability. To address these challenges, we modelthe cartilages structure and appearance from knee MRI into a graphrepresentation, which is capable of handling highly diverse clinical data.Then, guided by the cartilage graph representation, we design a non-Euclideandeep learning network with the self-attention mechanism, to extract cartilagefeatures in the local and global, and to derive the final assessment with avisualized result. Our comprehensive experiments show that the proposed methodyields superior performance in knee cartilage defect assessment, plus itsconvenient 3D visualization for interpretability.

Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution