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Artificial intellig...
Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies
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- Molnar, David (author)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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- Enqvist, Olof, 1981 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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Ulén, Johannes (author)
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Larsson, Måns, 1989 (author)
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- Brandberg, John, 1966 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för radiologi,Institute of Clinical Sciences, Department of Radiology
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- Johnsson, Åse (Allansdotter), 1966 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för radiologi,Institute of Clinical Sciences, Department of Radiology
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- Björnson, Elias, 1988 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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- Bergström, Göran, 1964 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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- Hjelmgren, Ola (author)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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(creator_code:org_t)
- 2021-12-13
- 2021
- English.
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In: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 11:1
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Abstract
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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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine (hsv//eng)
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- art (subject category)
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- By the author/editor
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Molnar, David
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Enqvist, Olof, 1 ...
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Ulén, Johannes
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Larsson, Måns, 1 ...
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Brandberg, John, ...
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Johnsson, Åse (A ...
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Björnson, Elias, ...
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Bergström, Göran ...
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Hjelmgren, Ola
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- About the subject
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Computer and Inf ...
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and Computer Vision ...
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- ENGINEERING AND TECHNOLOGY
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ENGINEERING AND ...
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and Medical Engineer ...
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and Medical Image Pr ...
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
- Articles in the publication
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Scientific Repor ...
- By the university
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Chalmers University of Technology
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University of Gothenburg