High-quality annotations for deep learning enabled plaque analysis in SCAPIS cardiac computed tomography angiography
Artikel i vetenskaplig tidskrift, 2023

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.

Coronary plaque analysis

Coronary Computed Tomography Angiography

Deep Learning

Annotated dataset

Författare

Erika Fagman

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Jennifer Alvén

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Göteborgs universitet

Johan Westerbergh

Uppsala Clinical Research Center

Pieter Kitslaar

Medis medical imaging systems bv

Michael Kercsik

Alingsås Lasarett

Göteborgs universitet

Kerstin Cederlund

Karolinska Institutet

Olov Duvernoy

Uppsala universitet

Jan Engvall

Linköpings universitet

Isabel Goncalves

Skånes universitetssjukhus (SUS)

Lunds universitet

Hanna Markstad

Lunds universitet

Ellen Ostenfeld

Lunds universitet

Göran Bergström

Göteborgs universitet

Sahlgrenska universitetssjukhuset

Ola Hjelmgren

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Heliyon

24058440 (eISSN)

Vol. 9 5 e16058

Ämneskategorier

Kardiologi

Radiologi och bildbehandling

Medicinsk bildbehandling

DOI

10.1016/j.heliyon.2023.e16058

PubMed

37215775

Mer information

Senast uppdaterat

2023-05-30