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

Novel Coronavirus disease (COVID-19) is a highly contagious respiratoryinfection that has had devastating effects on the world. Recently, new COVID-19variants are emerging making the situation more challenging and threatening.Evaluation and quantification of COVID-19 lung abnormalities based on chestComputed Tomography (CT) scans can help determining the disease stage,efficiently allocating limited healthcare resources, and making informedtreatment decisions. During pandemic era, however, visual assessment andquantification of COVID-19 lung lesions by expert radiologists become expensiveand prone to error, which raises an urgent quest to develop practicalautonomous solutions. In this context, first, the paper introduces an openaccess COVID-19 CT segmentation dataset containing 433 CT images from 82patients that have been annotated by an expert radiologist. Second, a DeepNeural Network (DNN)-based framework is proposed, referred to as theCOVID-Rate, that autonomously segments lung abnormalities associated withCOVID-19 from chest CT scans. Performance of the proposed COVID-Rate frameworkis evaluated through several experiments based on the introduced and externaldatasets. The results show a dice score of 0:802 and specificity andsensitivity of 0:997 and 0:832, respectively. Furthermore, the results indicatethat the COVID-Rate model can efficiently segment COVID-19 lesions in both 2DCT images and whole lung volumes. Results on the external dataset illustrategeneralization capabilities of the COVID-Rate model to CT images obtained froma different scanner.

COVID-Rate: An Automated Framework for Segmentation of COVID-19 Lesions from Chest CT Scans