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

Quantitative analysis of vessel wall structures by automated vessel wallsegmentation provides useful imaging biomarkers in evaluating atheroscleroticlesions and plaque progression time-efficiently. To quantify vessel wallfeatures, drawing lumen and outer wall contours of the artery of interest isrequired. To alleviate manual labor in contour drawing, some computer-assistedtools exist, but manual preprocessing steps, such as region of interestidentification and boundary initialization are needed. In addition, the priorknowledge of the ring shape of vessel wall is not taken into consideration indesigning the segmentation method. In this work, trained on manual vessel wallcontours, a fully automated artery localization and vessel wall segmentationsystem is proposed. A tracklet refinement algorithm is used to robustlyidentify the centerlines of arteries of interest from a neural networklocalization architecture. Image patches are extracted from the centerlines andsegmented in a polar coordinate system to use 3D information and to overcomeproblems such as contour discontinuity and interference from neighboringvessels. From a carotid artery dataset with 116 subjects (3406 slices) and apopliteal artery dataset with 5 subjects (289 slices), the proposed system isshown to robustly identify the artery of interest and segment the vessel wall.The proposed system demonstrates better performance on the carotid dataset witha Dice similarity coefficient of 0.824, compared with traditional vessel wallsegmentation methods, Dice of 0.576, and traditional convolutional neuralnetwork approaches, Dice of 0.747. This vessel wall segmentation system willfacilitate research on atherosclerosis and assist radiologists in image review.

Automated Artery Localization and Vessel Wall Segmentation of Magnetic Resonance Vessel Wall Images using Tracklet Refinement and Polar Conversion