Ontology type: schema:ScholarlyArticle
2014-07
AUTHORSFlorence Morin-Roy, Claude Kauffmann, An Tang, Sofiane Hadjadj, Olivier Thomas, Nicolas Piché, Stéphane Elkouri, Dan Yang Yang, Éric Therasse, Gilles Soulez
ABSTRACTPURPOSE: To assess the impact of contrast injection and stent-graft implantation on feasibility, accuracy, and reproducibility of abdominal aortic aneurysm (AAA) volume and maximal diameter (D-max) measurements using segmentation software. MATERIALS AND METHODS: CT images of 80 subjects presenting AAA were divided into four equal groups: with or without contrast enhancement, and with or without stent-graft implantation. Semiautomated software was used to segment the aortic wall, once by an expert and twice by three readers. Volume and D-max reproducibility was estimated by intraclass correlation coefficients (ICC), and accuracy was estimated between the expert and the readers by mean relative errors. RESULTS: All segmentations were technically successful. The mean AAA volume was 167.0 ± 82.8 mL and the mean D-max 55.0 ± 10.6 mm. Inter- and intraobserver ICCs for volume and D-max measurements were greater than 0.99. Mean relative errors between readers varied between -1.8 ± 4.6 and 0.0 ± 3.6 mL. Mean relative errors in volume and D-max measurements between readers showed no significant difference between the four groups (P ≥ 0.2). CONCLUSION: The feasibility, accuracy, and reproducibility of AAA volume and D-max measurements using segmentation software were not affected by the absence of contrast injection or the presence of stent-graft. KEY POINTS: • AAA volumetry by semiautomated segmentation is accurate on CT following endovascular repair. • AAA volumetry by semiautomated segmentation is accurate on unenhanced CT. • Standardization of the segmentation technique maximizes the reproducibility of volume measurements. More... »
PAGES1594-1601
http://scigraph.springernature.com/pub.10.1007/s00330-014-3175-0
DOIhttp://dx.doi.org/10.1007/s00330-014-3175-0
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PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/24801978
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"description": "PURPOSE: To assess the impact of contrast injection and stent-graft implantation on feasibility, accuracy, and reproducibility of abdominal aortic aneurysm (AAA) volume and maximal diameter (D-max) measurements using segmentation software.\nMATERIALS AND METHODS: CT images of 80 subjects presenting AAA were divided into four equal groups: with or without contrast enhancement, and with or without stent-graft implantation. Semiautomated software was used to segment the aortic wall, once by an expert and twice by three readers. Volume and D-max reproducibility was estimated by intraclass correlation coefficients (ICC), and accuracy was estimated between the expert and the readers by mean relative errors.\nRESULTS: All segmentations were technically successful. The mean AAA volume was 167.0\u2009\u00b1\u200982.8 mL and the mean D-max 55.0\u2009\u00b1\u200910.6 mm. Inter- and intraobserver ICCs for volume and D-max measurements were greater than 0.99. Mean relative errors between readers varied between -1.8\u2009\u00b1\u20094.6 and 0.0\u2009\u00b1\u20093.6 mL. Mean relative errors in volume and D-max measurements between readers showed no significant difference between the four groups (P\u2009\u2265\u20090.2).\nCONCLUSION: The feasibility, accuracy, and reproducibility of AAA volume and D-max measurements using segmentation software were not affected by the absence of contrast injection or the presence of stent-graft.\nKEY POINTS: \u2022 AAA volumetry by semiautomated segmentation is accurate on CT following endovascular repair. \u2022 AAA volumetry by semiautomated segmentation is accurate on unenhanced CT. \u2022 Standardization of the segmentation technique maximizes the reproducibility of volume measurements.",
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"pub.1026345620"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00330-014-3175-0",
"https://app.dimensions.ai/details/publication/pub.1026345620"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T19:01",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8678_00000481.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1007/s00330-014-3175-0"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00330-014-3175-0'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00330-014-3175-0'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00330-014-3175-0'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00330-014-3175-0'
This table displays all metadata directly associated to this object as RDF triples.
302 TRIPLES
21 PREDICATES
74 URIs
40 LITERALS
28 BLANK NODES