FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2981843404> ?p ?o ?g. }

• W2981843404 endingPage "105150" @default.
• W2981843404 startingPage "105150" @default.
• W2981843404 abstract "Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to efficient quantification of perfusion defects. The purpose of this study was to investigate the usefulness of uncertainty estimation in deep convolutional neural networks for automatic myocardial segmentation. A U-Net segmentation model was trained on the cardiac cine data. Monte Carlo dropout sampling of the U-Net model was performed on the dynamic perfusion datasets frame-by-frame to estimate the standard deviation (SD) maps. The uncertainty estimate based on the sum of the SD values was used to select the optimal frames for endocardial and epicardial segmentations. DCE perfusion data from 35 subjects (14 subjects with coronary artery disease, 8 subjects with hypertrophic cardiomyopathy, and 13 healthy volunteers) were evaluated. The Dice similarity scores of the proposed method were compared with those of a semi-automatic U-Net segmentation method, which involved user selection of an image frame for segmentation in the cardiac perfusion dataset. The proposed method was fully automatic and did not require manual labeling of the cardiac perfusion image data for model development. The mean Dice similarity score of the proposed automatic method was 0.806 (±0.096), which was comparable to the 0.808 (±0.084) Dice score of the semi-automatic U-Net segmentation method (intraclass correlation coefficient = 0.61, P < 0.001). Our study demonstrated the feasibility of applying an existing model trained on cardiac cine data to dynamic cardiac perfusion data to achieve robust and automatic segmentation of the myocardium. The uncertainty estimates can be used for screening purposes, which would facilitate the cases with high endocardial and epicardial uncertainty estimates to be sent for further evaluation and correction by human experts." @default.
• W2981843404 created "2019-11-01" @default.
• W2981843404 creator A5075598145 @default.
• W2981843404 creator A5078714365 @default.
• W2981843404 creator A5086152357 @default.
• W2981843404 date "2020-03-01" @default.
• W2981843404 modified "2023-09-25" @default.
• W2981843404 title "Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network" @default.
• W2981843404 cites W1988419754 @default.
• W2981843404 cites W2029098063 @default.
• W2981843404 cites W2042099911 @default.
• W2981843404 cites W2061620122 @default.
• W2981843404 cites W2129884887 @default.
• W2981843404 cites W2131466065 @default.
• W2981843404 cites W2149755434 @default.
• W2981843404 cites W2160133249 @default.
• W2981843404 cites W2170059438 @default.
• W2981843404 cites W2255189008 @default.
• W2981843404 cites W2274227799 @default.
• W2981843404 cites W2278352592 @default.
• W2981843404 cites W2318686907 @default.
• W2981843404 cites W2404618390 @default.
• W2981843404 cites W2606576226 @default.
• W2981843404 cites W2757633676 @default.
• W2981843404 cites W2904746676 @default.
• W2981843404 cites W2939337927 @default.
• W2981843404 cites W2951464021 @default.
• W2981843404 cites W2951965145 @default.
• W2981843404 cites W2963881378 @default.
• W2981843404 cites W4382891181 @default.
• W2981843404 doi "https://doi.org/10.1016/j.cmpb.2019.105150" @default.
• W2981843404 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31671341" @default.
• W2981843404 hasPublicationYear "2020" @default.
• W2981843404 type Work @default.
• W2981843404 sameAs 2981843404 @default.
• W2981843404 citedByCount "14" @default.
• W2981843404 countsByYear W29818434042020 @default.
• W2981843404 countsByYear W29818434042021 @default.
• W2981843404 countsByYear W29818434042022 @default.
• W2981843404 countsByYear W29818434042023 @default.
• W2981843404 crossrefType "journal-article" @default.
• W2981843404 hasAuthorship W2981843404A5075598145 @default.
• W2981843404 hasAuthorship W2981843404A5078714365 @default.
• W2981843404 hasAuthorship W2981843404A5086152357 @default.
• W2981843404 hasConcept C104709138 @default.
• W2981843404 hasConcept C124504099 @default.
• W2981843404 hasConcept C126838900 @default.
• W2981843404 hasConcept C146957229 @default.
• W2981843404 hasConcept C153180895 @default.
• W2981843404 hasConcept C154945302 @default.
• W2981843404 hasConcept C163892561 @default.
• W2981843404 hasConcept C171606756 @default.
• W2981843404 hasConcept C41008148 @default.
• W2981843404 hasConcept C70410870 @default.
• W2981843404 hasConcept C71924100 @default.
• W2981843404 hasConcept C81363708 @default.
• W2981843404 hasConcept C89600930 @default.
• W2981843404 hasConceptScore W2981843404C104709138 @default.
• W2981843404 hasConceptScore W2981843404C124504099 @default.
• W2981843404 hasConceptScore W2981843404C126838900 @default.
• W2981843404 hasConceptScore W2981843404C146957229 @default.
• W2981843404 hasConceptScore W2981843404C153180895 @default.
• W2981843404 hasConceptScore W2981843404C154945302 @default.
• W2981843404 hasConceptScore W2981843404C163892561 @default.
• W2981843404 hasConceptScore W2981843404C171606756 @default.
• W2981843404 hasConceptScore W2981843404C41008148 @default.
• W2981843404 hasConceptScore W2981843404C70410870 @default.
• W2981843404 hasConceptScore W2981843404C71924100 @default.
• W2981843404 hasConceptScore W2981843404C81363708 @default.
• W2981843404 hasConceptScore W2981843404C89600930 @default.
• W2981843404 hasLocation W29818434041 @default.
• W2981843404 hasOpenAccess W2981843404 @default.
• W2981843404 hasPrimaryLocation W29818434041 @default.
• W2981843404 hasRelatedWork W2138214894 @default.
• W2981843404 hasRelatedWork W2549765251 @default.
• W2981843404 hasRelatedWork W2767651786 @default.
• W2981843404 hasRelatedWork W2769435486 @default.
• W2981843404 hasRelatedWork W2912288872 @default.
• W2981843404 hasRelatedWork W2948809999 @default.
• W2981843404 hasRelatedWork W2999580839 @default.
• W2981843404 hasRelatedWork W3161321444 @default.
• W2981843404 hasRelatedWork W4200528772 @default.
• W2981843404 hasRelatedWork W564581980 @default.
• W2981843404 hasVolume "185" @default.
• W2981843404 isParatext "false" @default.
• W2981843404 isRetracted "false" @default.
• W2981843404 magId "2981843404" @default.
• W2981843404 workType "article" @default.