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Artificial intellig...
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Molnar, DavidGothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
(author)
Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies
- Article/chapterEnglish2021
Publisher, publication year, extent ...
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2021-12-13
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Springer Science and Business Media LLC,2021
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electronicrdacarrier
Numbers
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LIBRIS-ID:oai:research.chalmers.se:6617d552-b724-41ba-a629-baddf19038df
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https://doi.org/10.1038/s41598-021-03150-wDOI
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https://research.chalmers.se/publication/527776URI
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https://gup.ub.gu.se/publication/311744URI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:art swepub-publicationtype
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Subject category:ref swepub-contenttype
Notes
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To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT images from the SCAPIS study were used to train and test a convolutional neural network based model to quantify EAT by: segmenting the pericardium, suppressing noise-induced artifacts in the heart chambers, and, if image sets were incomplete, imputing missing EAT volumes. The model achieved a mean Dice coefficient of 0.90 when tested against expert manual segmentations on 25 image sets. Tested on 1400 image sets, the model successfully segmented 99.4% of the cases. Automatic imputation of missing EAT volumes had an error of less than 3.1% with up to 20% of the slices in image sets missing. The most important predictors of EAT volumes were weight and waist, while EAT attenuation was predicted mainly by EAT volume. A model with excellent performance, capable of fully automatic handling of the most common challenges in large scale EAT quantification has been developed. In studies of the importance of EAT in disease development, the strong co-variation with anthropometric measures needs to be carefully considered.
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Enqvist, Olof,1981Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)enolof
(author)
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Ulén, Johannes
(author)
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Larsson, Måns,1989(Swepub:cth)lmans
(author)
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Brandberg, John,1966Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för radiologi,Institute of Clinical Sciences, Department of Radiology(Swepub:gu)xbrjoh
(author)
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Johnsson, Åse (Allansdotter),1966Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för radiologi,Institute of Clinical Sciences, Department of Radiology(Swepub:gu)xjohna
(author)
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Björnson, Elias,1988Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine(Swepub:gu)xbelia
(author)
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Bergström, Göran,1964Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine(Swepub:gu)xbgort
(author)
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Hjelmgren, OlaGothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine(Swepub:gu)xhjelo
(author)
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Göteborgs universitetInstitutionen för medicin, avdelningen för molekylär och klinisk medicin
(creator_code:org_t)
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In:Scientific Reports: Springer Science and Business Media LLC11:12045-23222045-2322
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