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Automated quality-controlled cardiovascular magnetic resonance pericardial fat quantification using a convolutional neural network in the UK Biobank.

Automated quality-controlled cardiovascular magnetic resonance pericardial fat quantification using a convolutional neural network in the UK Biobank.
Automated quality-controlled cardiovascular magnetic resonance pericardial fat quantification using a convolutional neural network in the UK Biobank.
Background: Pericardial adipose tissue (PAT) may represent a novel risk marker for cardiovascular disease. However, absence of rapid radiation-free PAT quantification methods has precluded its examination in large cohorts.

Objectives: We developed a fully automated quality-controlled tool for cardiovascular magnetic resonance (CMR) PAT quantification in the UK Biobank (UKB).

Methods: Image analysis comprised contouring an en-bloc PAT area on four-chamber cine images. We created a ground truth manual analysis dataset randomly split into training and test sets. We built a neural network for automated segmentation using a Multi-residual U-net architecture with incorporation of permanently active dropout layers to facilitate quality control of the model's output using Monte Carlo sampling. We developed an in-built quality control feature, which presents predicted Dice scores. We evaluated model performance against the test set (n = 87), the whole UKB Imaging cohort (n = 45,519), and an external dataset (n = 103). In an independent dataset, we compared automated CMR and cardiac computed tomography (CCT) PAT quantification. Finally, we tested association of CMR PAT with diabetes in the UKB (n = 42,928).

Results: Agreement between automated and manual segmentations in the test set was almost identical to inter-observer variability (mean Dice score = 0.8). The quality control method predicted individual Dice scores with Pearson r = 0.75. Model performance remained high in the whole UKB Imaging cohort and in the external dataset, with medium–good quality segmentation in 94.3% (mean Dice score = 0.77) and 94.4% (mean Dice score = 0.78), respectively. There was high correlation between CMR and CCT PAT measures (Pearson r = 0.72, p-value 5.3 ×10−18). Larger CMR PAT area was associated with significantly greater odds of diabetes independent of age, sex, and body mass index.

Conclusions: We present a novel fully automated method for CMR PAT quantification with good model performance on independent and external datasets, high correlation with reference standard CCT PAT measurement, and expected clinical associations with diabetes.

automated image analysis, cardiovascular magnetic resonance, epicardial fat, machine learning, neural network, obesity, pericardial fat
Bard, Andrew
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Raisi-Estabragh, Zahra
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Ardissino, Maddalena
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Lee, Aaron Mark
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Pugliese, Francesca
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Dey, Damini
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Sarkar, Sandip
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Munroe, Patricia B.
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Neubauer, Stefan
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Harvey, Nicholas
ce487fb4-d360-4aac-9d17-9466d6cba145
Petersen, Steffen E.
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Bard, Andrew
5ace4cbc-98c0-4419-b317-0b062358b237
Raisi-Estabragh, Zahra
43c85c5e-4574-476b-80d6-8fb1cdb3df0a
Ardissino, Maddalena
2cfd4d91-f405-4ed0-912b-313b0316c0fb
Lee, Aaron Mark
58a0fb9e-1bbe-4a73-bbcd-9103d4fa4fc6
Pugliese, Francesca
15788b28-a1b4-4337-8042-9da62750dfe4
Dey, Damini
2189dc96-e4cd-4a40-b3ea-24d7e5eb98b4
Sarkar, Sandip
5028a70c-7e6d-474a-a695-58c078df665b
Munroe, Patricia B.
44d23746-20cd-4572-860e-7350424cc031
Neubauer, Stefan
c8a34156-a4ed-4dfe-97cb-4f47627d927d
Harvey, Nicholas
ce487fb4-d360-4aac-9d17-9466d6cba145
Petersen, Steffen E.
04f2ce88-790d-48dc-baac-cbe0946dd928

Bard, Andrew, Raisi-Estabragh, Zahra, Ardissino, Maddalena, Lee, Aaron Mark, Pugliese, Francesca, Dey, Damini, Sarkar, Sandip, Munroe, Patricia B., Neubauer, Stefan, Harvey, Nicholas and Petersen, Steffen E. (2021) Automated quality-controlled cardiovascular magnetic resonance pericardial fat quantification using a convolutional neural network in the UK Biobank. Frontiers in Cardiovascular Medicine, 8, [677574]. (doi:10.3389/fcvm.2021.677574).

Record type: Article

Abstract

Background: Pericardial adipose tissue (PAT) may represent a novel risk marker for cardiovascular disease. However, absence of rapid radiation-free PAT quantification methods has precluded its examination in large cohorts.

Objectives: We developed a fully automated quality-controlled tool for cardiovascular magnetic resonance (CMR) PAT quantification in the UK Biobank (UKB).

Methods: Image analysis comprised contouring an en-bloc PAT area on four-chamber cine images. We created a ground truth manual analysis dataset randomly split into training and test sets. We built a neural network for automated segmentation using a Multi-residual U-net architecture with incorporation of permanently active dropout layers to facilitate quality control of the model's output using Monte Carlo sampling. We developed an in-built quality control feature, which presents predicted Dice scores. We evaluated model performance against the test set (n = 87), the whole UKB Imaging cohort (n = 45,519), and an external dataset (n = 103). In an independent dataset, we compared automated CMR and cardiac computed tomography (CCT) PAT quantification. Finally, we tested association of CMR PAT with diabetes in the UKB (n = 42,928).

Results: Agreement between automated and manual segmentations in the test set was almost identical to inter-observer variability (mean Dice score = 0.8). The quality control method predicted individual Dice scores with Pearson r = 0.75. Model performance remained high in the whole UKB Imaging cohort and in the external dataset, with medium–good quality segmentation in 94.3% (mean Dice score = 0.77) and 94.4% (mean Dice score = 0.78), respectively. There was high correlation between CMR and CCT PAT measures (Pearson r = 0.72, p-value 5.3 ×10−18). Larger CMR PAT area was associated with significantly greater odds of diabetes independent of age, sex, and body mass index.

Conclusions: We present a novel fully automated method for CMR PAT quantification with good model performance on independent and external datasets, high correlation with reference standard CCT PAT measurement, and expected clinical associations with diabetes.

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More information

Accepted/In Press date: 17 May 2021
e-pub ahead of print date: 7 July 2021
Published date: 7 July 2021
Keywords: automated image analysis, cardiovascular magnetic resonance, epicardial fat, machine learning, neural network, obesity, pericardial fat

Identifiers

Local EPrints ID: 450298
URI: http://eprints.soton.ac.uk/id/eprint/450298
PURE UUID: 3f11eb66-0379-446b-9463-4cf89d8bd37d
ORCID for Nicholas Harvey: ORCID iD orcid.org/0000-0002-8194-2512

Catalogue record

Date deposited: 21 Jul 2021 16:30
Last modified: 11 Nov 2021 02:40

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Contributors

Author: Andrew Bard
Author: Zahra Raisi-Estabragh
Author: Maddalena Ardissino
Author: Aaron Mark Lee
Author: Francesca Pugliese
Author: Damini Dey
Author: Sandip Sarkar
Author: Patricia B. Munroe
Author: Stefan Neubauer
Author: Nicholas Harvey ORCID iD
Author: Steffen E. Petersen

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