FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2580724397> ?p ?o ?g. }

• W2580724397 endingPage "1049" @default.
• W2580724397 startingPage "1040" @default.
• W2580724397 abstract "Purpose The goal of this study was to assess the potential added benefit of accounting for partial volume effects ( PVE ) in an automatic coronary lumen segmentation algorithm that is used to determine the hemodynamic significance of a coronary artery stenosis from coronary computed tomography angiography ( CCTA ). Materials and methods Two sets of data were used in our work: (a) multivendor CCTA datasets of 18 subjects from the MICCAI 2012 challenge with automatically generated centerlines and 3 reference segmentations of 78 coronary segments and (b) additional CCTA datasets of 97 subjects with 132 coronary lesions that had invasive reference standard FFR measurements. We extracted the coronary artery centerlines for the 97 datasets by an automated software program followed by manual correction if required. An automatic machine‐learning‐based algorithm segmented the coronary tree with and without accounting for the PVE . We obtained CCTA ‐based FFR measurements using a flow simulation in the coronary trees that were generated by the automatic algorithm with and without accounting for PVE . We assessed the potential added value of PVE integration as a part of the automatic coronary lumen segmentation algorithm by means of segmentation accuracy using the MICCAI 2012 challenge framework and by means of flow simulation overall accuracy, sensitivity, specificity, negative and positive predictive values, and the receiver operated characteristic ( ROC ) area under the curve. We also evaluated the potential benefit of accounting for PVE in automatic segmentation for flow simulation for lesions that were diagnosed as obstructive based on CCTA which could have indicated a need for an invasive exam and revascularization. Results Our segmentation algorithm improves the maximal surface distance error by ~39% compared to previously published method on the 18 datasets from the MICCAI 2012 challenge with comparable Dice and mean surface distance. Results with and without accounting for PVE were comparable. In contrast, integrating PVE analysis into an automatic coronary lumen segmentation algorithm improved the flow simulation specificity from 0.6 to 0.68 with the same sensitivity of 0.83. Also, accounting for PVE improved the area under the ROC curve for detecting hemodynamically significant CAD from 0.76 to 0.8 compared to automatic segmentation without PVE analysis with invasive FFR threshold of 0.8 as the reference standard. Accounting for PVE in flow simulation to support the detection of hemodynamic significant disease in CCTA ‐based obstructive lesions improved specificity from 0.51 to 0.73 with same sensitivity of 0.83 and the area under the curve from 0.69 to 0.79. The improvement in the AUC was statistically significant ( N = 76, Delong's test, P = 0.012). Conclusion Accounting for the partial volume effects in automatic coronary lumen segmentation algorithms has the potential to improve the accuracy of CCTA ‐based hemodynamic assessment of coronary artery lesions." @default.
• W2580724397 created "2017-02-03" @default.
• W2580724397 creator A5011834104 @default.
• W2580724397 creator A5041524969 @default.
• W2580724397 creator A5043188797 @default.
• W2580724397 creator A5079290904 @default.
• W2580724397 creator A5085176678 @default.
• W2580724397 creator A5088607562 @default.
• W2580724397 creator A5089641146 @default.
• W2580724397 creator A5091313305 @default.
• W2580724397 date "2017-03-01" @default.
• W2580724397 modified "2023-10-18" @default.
• W2580724397 title "Improving <scp>CCTA</scp>‐based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation" @default.
• W2580724397 cites W127494906 @default.
• W2580724397 cites W1540562167 @default.
• W2580724397 cites W170832280 @default.
• W2580724397 cites W1866040239 @default.
• W2580724397 cites W1969767460 @default.
• W2580724397 cites W1974954013 @default.
• W2580724397 cites W1995366196 @default.
• W2580724397 cites W1997341395 @default.
• W2580724397 cites W200744690 @default.
• W2580724397 cites W2055005164 @default.
• W2580724397 cites W2057561283 @default.
• W2580724397 cites W2082304218 @default.
• W2580724397 cites W2095680098 @default.
• W2580724397 cites W2118810625 @default.
• W2580724397 cites W2119300483 @default.
• W2580724397 cites W2122111042 @default.
• W2580724397 cites W2130153488 @default.
• W2580724397 cites W2131132431 @default.
• W2580724397 cites W2134965089 @default.
• W2580724397 cites W2141408670 @default.
• W2580724397 cites W2161213346 @default.
• W2580724397 cites W2165023525 @default.
• W2580724397 cites W2168005337 @default.
• W2580724397 cites W2171963641 @default.
• W2580724397 cites W2173487411 @default.
• W2580724397 cites W2185291575 @default.
• W2580724397 cites W2186191235 @default.
• W2580724397 cites W2276422413 @default.
• W2580724397 cites W2328176404 @default.
• W2580724397 cites W2407173596 @default.
• W2580724397 doi "https://doi.org/10.1002/mp.12121" @default.
• W2580724397 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28112409" @default.
• W2580724397 hasPublicationYear "2017" @default.
• W2580724397 type Work @default.
• W2580724397 sameAs 2580724397 @default.
• W2580724397 citedByCount "22" @default.
• W2580724397 countsByYear W25807243972017 @default.
• W2580724397 countsByYear W25807243972018 @default.
• W2580724397 countsByYear W25807243972019 @default.
• W2580724397 countsByYear W25807243972021 @default.
• W2580724397 countsByYear W25807243972022 @default.
• W2580724397 countsByYear W25807243972023 @default.
• W2580724397 crossrefType "journal-article" @default.
• W2580724397 hasAuthorship W2580724397A5011834104 @default.
• W2580724397 hasAuthorship W2580724397A5041524969 @default.
• W2580724397 hasAuthorship W2580724397A5043188797 @default.
• W2580724397 hasAuthorship W2580724397A5079290904 @default.
• W2580724397 hasAuthorship W2580724397A5085176678 @default.
• W2580724397 hasAuthorship W2580724397A5088607562 @default.
• W2580724397 hasAuthorship W2580724397A5089641146 @default.
• W2580724397 hasAuthorship W2580724397A5091313305 @default.
• W2580724397 hasBestOaLocation W25807243972 @default.
• W2580724397 hasConcept C11413529 @default.
• W2580724397 hasConcept C126322002 @default.
• W2580724397 hasConcept C126838900 @default.
• W2580724397 hasConcept C131631996 @default.
• W2580724397 hasConcept C154945302 @default.
• W2580724397 hasConcept C164705383 @default.
• W2580724397 hasConcept C2777987666 @default.
• W2580724397 hasConcept C2778213512 @default.
• W2580724397 hasConcept C3019004856 @default.
• W2580724397 hasConcept C41008148 @default.
• W2580724397 hasConcept C500558357 @default.
• W2580724397 hasConcept C71924100 @default.
• W2580724397 hasConcept C82233179 @default.
• W2580724397 hasConcept C89600930 @default.
• W2580724397 hasConceptScore W2580724397C11413529 @default.
• W2580724397 hasConceptScore W2580724397C126322002 @default.
• W2580724397 hasConceptScore W2580724397C126838900 @default.
• W2580724397 hasConceptScore W2580724397C131631996 @default.
• W2580724397 hasConceptScore W2580724397C154945302 @default.
• W2580724397 hasConceptScore W2580724397C164705383 @default.
• W2580724397 hasConceptScore W2580724397C2777987666 @default.
• W2580724397 hasConceptScore W2580724397C2778213512 @default.
• W2580724397 hasConceptScore W2580724397C3019004856 @default.
• W2580724397 hasConceptScore W2580724397C41008148 @default.
• W2580724397 hasConceptScore W2580724397C500558357 @default.
• W2580724397 hasConceptScore W2580724397C71924100 @default.
• W2580724397 hasConceptScore W2580724397C82233179 @default.
• W2580724397 hasConceptScore W2580724397C89600930 @default.
• W2580724397 hasIssue "3" @default.
• W2580724397 hasLocation W25807243971 @default.
• W2580724397 hasLocation W25807243972 @default.
• W2580724397 hasLocation W25807243973 @default.
• W2580724397 hasLocation W25807243974 @default.

• next