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High-quality annotations for deep learning enabled plaque analysis in SCAPIS cardiac computed tomography angiography

Fagman, Erika ; Alvén, Jennifer ; Westerbergh, Johan ; Kitslaar, Pieter ; Kercsik, Michael ; Cederlund, Kerstin ; Duvernoy, Olov ; Engvall, Jan ; Gonçalves, Isabel LU orcid and Markstad, Hanna LU , et al. (2023) In Heliyon 9(5).
Abstract

Background: Plaque analysis with coronary computed tomography angiography (CCTA) is a promising tool to identify high risk of future coronary events. The analysis process is time-consuming, and requires highly trained readers. Deep learning models have proved to excel at similar tasks, however, training these models requires large sets of expert-annotated training data. The aims of this study were to generate a large, high-quality annotated CCTA dataset derived from Swedish CArdioPulmonary BioImage Study (SCAPIS), report the reproducibility of the annotation core lab and describe the plaque characteristics and their association with established risk factors. Methods and results: The coronary artery tree was manually segmented using... (More)

Background: Plaque analysis with coronary computed tomography angiography (CCTA) is a promising tool to identify high risk of future coronary events. The analysis process is time-consuming, and requires highly trained readers. Deep learning models have proved to excel at similar tasks, however, training these models requires large sets of expert-annotated training data. The aims of this study were to generate a large, high-quality annotated CCTA dataset derived from Swedish CArdioPulmonary BioImage Study (SCAPIS), report the reproducibility of the annotation core lab and describe the plaque characteristics and their association with established risk factors. Methods and results: The coronary artery tree was manually segmented using semi-automatic software by four primary and one senior secondary reader. A randomly selected sample of 469 subjects, all with coronary plaques and stratified for cardiovascular risk using the Systematic Coronary Risk Evaluation (SCORE), were analyzed. The reproducibility study (n = 78) showed an agreement for plaque detection of 0.91 (0.84–0.97). The mean percentage difference for plaque volumes was −0.6% the mean absolute percentage difference 19.4% (CV 13.7%, ICC 0.94). There was a positive correlation between SCORE and total plaque volume (rho = 0.30, p < 0.001) and total low attenuation plaque volume (rho = 0.29, p < 0.001). Conclusions: We have generated a CCTA dataset with high-quality plaque annotations showing good reproducibility and an expected correlation between plaque features and cardiovascular risk. The stratified data sampling has enriched high-risk plaques making the data well suited as training, validation and test data for a fully automatic analysis tool based on deep learning.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Annotated dataset, Coronary Computed Tomography Angiography, Coronary plaque analysis, Deep Learning
in
Heliyon
volume
9
issue
5
article number
e16058
publisher
Elsevier
external identifiers
  • pmid:37215775
  • scopus:85159126906
ISSN
2405-8440
DOI
10.1016/j.heliyon.2023.e16058
language
English
LU publication?
yes
id
d9d8166c-2676-4179-b044-551abbd643fd
date added to LUP
2023-08-11 09:13:06
date last changed
2024-04-20 00:23:58
@article{d9d8166c-2676-4179-b044-551abbd643fd,
  abstract     = {{<p>Background: Plaque analysis with coronary computed tomography angiography (CCTA) is a promising tool to identify high risk of future coronary events. The analysis process is time-consuming, and requires highly trained readers. Deep learning models have proved to excel at similar tasks, however, training these models requires large sets of expert-annotated training data. The aims of this study were to generate a large, high-quality annotated CCTA dataset derived from Swedish CArdioPulmonary BioImage Study (SCAPIS), report the reproducibility of the annotation core lab and describe the plaque characteristics and their association with established risk factors. Methods and results: The coronary artery tree was manually segmented using semi-automatic software by four primary and one senior secondary reader. A randomly selected sample of 469 subjects, all with coronary plaques and stratified for cardiovascular risk using the Systematic Coronary Risk Evaluation (SCORE), were analyzed. The reproducibility study (n = 78) showed an agreement for plaque detection of 0.91 (0.84–0.97). The mean percentage difference for plaque volumes was −0.6% the mean absolute percentage difference 19.4% (CV 13.7%, ICC 0.94). There was a positive correlation between SCORE and total plaque volume (rho = 0.30, p &lt; 0.001) and total low attenuation plaque volume (rho = 0.29, p &lt; 0.001). Conclusions: We have generated a CCTA dataset with high-quality plaque annotations showing good reproducibility and an expected correlation between plaque features and cardiovascular risk. The stratified data sampling has enriched high-risk plaques making the data well suited as training, validation and test data for a fully automatic analysis tool based on deep learning.</p>}},
  author       = {{Fagman, Erika and Alvén, Jennifer and Westerbergh, Johan and Kitslaar, Pieter and Kercsik, Michael and Cederlund, Kerstin and Duvernoy, Olov and Engvall, Jan and Gonçalves, Isabel and Markstad, Hanna and Ostenfeld, Ellen and Bergström, Göran and Hjelmgren, Ola}},
  issn         = {{2405-8440}},
  keywords     = {{Annotated dataset; Coronary Computed Tomography Angiography; Coronary plaque analysis; Deep Learning}},
  language     = {{eng}},
  number       = {{5}},
  publisher    = {{Elsevier}},
  series       = {{Heliyon}},
  title        = {{High-quality annotations for deep learning enabled plaque analysis in SCAPIS cardiac computed tomography angiography}},
  url          = {{http://dx.doi.org/10.1016/j.heliyon.2023.e16058}},
  doi          = {{10.1016/j.heliyon.2023.e16058}},
  volume       = {{9}},
  year         = {{2023}},
}