FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3164821526> ?p ?o ?g. }

• W3164821526 endingPage "4290" @default.
• W3164821526 startingPage "4279" @default.
• W3164821526 abstract "Purpose Epicardial fat is the adipose tissue between the serosal pericardial wall layer and the visceral layer. It is distributed mainly around the atrioventricular groove, atrial septum, ventricular septum and coronary arteries. Studies have shown that the density, thickness, volume and other characteristics of epicardial adipose tissue (EAT) are independently correlated with a variety of cardiovascular diseases. Given this association, the accurate determination of EAT volume is an essential aim of future research. Therefore, the purpose of this study was to establish a framework for fully automatic EAT segmentation and quantification in coronary computed tomography angiography (CCTA) scans. Methods A set of 103 scans are randomly selected from our medical center. An automatic pipeline has been developed to segment and quantify the volume of EAT. First, a multi-slice deep neural network is used to simultaneously segment the pericardium in multiple adjacent slices. Then a deformable model is employed to reduce false positive and negative regions in the segmented binary pericardial images. Finally, the pericardium mask is used to define the region of interest (ROI) and the threshold method is utilized to extract the pixels ranging from −175 Hounsfield units (HU) to −15 HU for the segmentation of EAT. Results The Dice indices of the pericardial segmentation using the proposed method with respect to the manual delineation results of two radiology experts were 97.1% 0.7% and 96.9% 0.6%, respectively. The inter-observer variability was also assessed, resulting in a Dice index of 97.0% 0.7%. For the EAT segmentation results, the Dice indices between the proposed method and the two radiology experts were 93.4% 1.5% and 93.3% 1.3%, respectively, and the same measurement between the experts themselves was 93.6% 1.9%. The Pearson’s correlation coefficients between the EAT volumes computed from the results of the proposed method and the manual delineation by the two experts were 1.00 and 0.99 and the same coefficients between the experts was 0.99. Conclusions This work describes the development of a fully automatic EAT segmentation and quantification method from CCTA scans and the results compare favorably with the assessments of two independent experts. The proposed method is also packaged with a graphical user interface which can be found at https://github.com/MountainAndMorning/EATSeg." @default.
• W3164821526 created "2021-06-07" @default.
• W3164821526 creator A5012704417 @default.
• W3164821526 creator A5013995996 @default.
• W3164821526 creator A5017735979 @default.
• W3164821526 creator A5023214008 @default.
• W3164821526 creator A5049570118 @default.
• W3164821526 creator A5053209536 @default.
• W3164821526 creator A5054922294 @default.
• W3164821526 creator A5079054774 @default.
• W3164821526 date "2021-06-29" @default.
• W3164821526 modified "2023-10-18" @default.
• W3164821526 title "Automatic quantification of epicardial adipose tissue volume" @default.
• W3164821526 cites W1856792630 @default.
• W3164821526 cites W1894830265 @default.
• W3164821526 cites W1901129140 @default.
• W3164821526 cites W1969004141 @default.
• W3164821526 cites W1977002622 @default.
• W3164821526 cites W1983071283 @default.
• W3164821526 cites W1989229614 @default.
• W3164821526 cites W1998855918 @default.
• W3164821526 cites W2008768241 @default.
• W3164821526 cites W2009414794 @default.
• W3164821526 cites W2015481227 @default.
• W3164821526 cites W2026394576 @default.
• W3164821526 cites W2026616100 @default.
• W3164821526 cites W2035143185 @default.
• W3164821526 cites W2035718613 @default.
• W3164821526 cites W2036175283 @default.
• W3164821526 cites W2038279598 @default.
• W3164821526 cites W2040445576 @default.
• W3164821526 cites W2043917892 @default.
• W3164821526 cites W2053642000 @default.
• W3164821526 cites W2058902680 @default.
• W3164821526 cites W2093945250 @default.
• W3164821526 cites W2100138047 @default.
• W3164821526 cites W2101689475 @default.
• W3164821526 cites W2104479126 @default.
• W3164821526 cites W2118606707 @default.
• W3164821526 cites W2145803225 @default.
• W3164821526 cites W2154470858 @default.
• W3164821526 cites W2157582714 @default.
• W3164821526 cites W2159520875 @default.
• W3164821526 cites W2206843839 @default.
• W3164821526 cites W2301358467 @default.
• W3164821526 cites W2464708700 @default.
• W3164821526 cites W2519781522 @default.
• W3164821526 cites W2735363562 @default.
• W3164821526 cites W2775292733 @default.
• W3164821526 cites W2790564346 @default.
• W3164821526 cites W2911020141 @default.
• W3164821526 cites W2937343562 @default.
• W3164821526 cites W2962914239 @default.
• W3164821526 cites W2972966746 @default.
• W3164821526 doi "https://doi.org/10.1002/mp.15012" @default.
• W3164821526 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34062000" @default.
• W3164821526 hasPublicationYear "2021" @default.
• W3164821526 type Work @default.
• W3164821526 sameAs 3164821526 @default.
• W3164821526 citedByCount "8" @default.
• W3164821526 countsByYear W31648215262022 @default.
• W3164821526 countsByYear W31648215262023 @default.
• W3164821526 crossrefType "journal-article" @default.
• W3164821526 hasAuthorship W3164821526A5012704417 @default.
• W3164821526 hasAuthorship W3164821526A5013995996 @default.
• W3164821526 hasAuthorship W3164821526A5017735979 @default.
• W3164821526 hasAuthorship W3164821526A5023214008 @default.
• W3164821526 hasAuthorship W3164821526A5049570118 @default.
• W3164821526 hasAuthorship W3164821526A5053209536 @default.
• W3164821526 hasAuthorship W3164821526A5054922294 @default.
• W3164821526 hasAuthorship W3164821526A5079054774 @default.
• W3164821526 hasConcept C126322002 @default.
• W3164821526 hasConcept C126838900 @default.
• W3164821526 hasConcept C154945302 @default.
• W3164821526 hasConcept C164705383 @default.
• W3164821526 hasConcept C171089720 @default.
• W3164821526 hasConcept C187954543 @default.
• W3164821526 hasConcept C2778875491 @default.
• W3164821526 hasConcept C2908987861 @default.
• W3164821526 hasConcept C2989005 @default.
• W3164821526 hasConcept C41008148 @default.
• W3164821526 hasConcept C544519230 @default.
• W3164821526 hasConcept C71924100 @default.
• W3164821526 hasConcept C89600930 @default.
• W3164821526 hasConceptScore W3164821526C126322002 @default.
• W3164821526 hasConceptScore W3164821526C126838900 @default.
• W3164821526 hasConceptScore W3164821526C154945302 @default.
• W3164821526 hasConceptScore W3164821526C164705383 @default.
• W3164821526 hasConceptScore W3164821526C171089720 @default.
• W3164821526 hasConceptScore W3164821526C187954543 @default.
• W3164821526 hasConceptScore W3164821526C2778875491 @default.
• W3164821526 hasConceptScore W3164821526C2908987861 @default.
• W3164821526 hasConceptScore W3164821526C2989005 @default.
• W3164821526 hasConceptScore W3164821526C41008148 @default.
• W3164821526 hasConceptScore W3164821526C544519230 @default.
• W3164821526 hasConceptScore W3164821526C71924100 @default.
• W3164821526 hasConceptScore W3164821526C89600930 @default.
• W3164821526 hasFunder F4320321001 @default.

• next