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

Cardiovascular disease (CVD) is a common and strong threat to human beings,featuring high prevalence, disability and mortality. The amount of coronaryartery calcification (CAC) is an effective factor for CVD risk evaluation.Conventionally, CAC is quantified using ECG-synchronized cardiac CT but rarelyfrom general chest CT scans. However, compared with ECG-synchronized cardiacCT, chest CT is more prevalent and economical in clinical practice. To addressthis, we propose an automatic method based on Dense U-Net to segment coronarycalcium pixels on both types of CT scans. Our contribution is two-fold. First,we propose a novel network called DenseRAUnet, which takes advantage of DenseU-net, ResNet and atrous convolutions. We prove the robustness andgeneralizability of our model by training it exclusively on chest CT while teston both types of CT scans. Second, we design a loss function combiningbootstrap with IoU function to balance foreground and background classes.DenseRAUnet is trained in a 2.5D fashion and tested on a private datasetconsisting of 144 scans. Results show an F1-score of 0.75, with 0.83 accuracyof predicting cardiovascular disease risk.

Automatic Calcium Scoring in Cardiac and Chest CT Using DenseRAUnet