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Automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography using nnU-Net

BACKGROUND: Epicardial adipose tissue (EAT) is a key aspect in the investigation of cardiac pathophysiology. We sought to develop a deep learning (DL) model for fully automatic extraction and quantification of EAT through pulmonary computed tomography venography (PCTV) images. METHODS: In this retro...

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Autores principales: Hu, Yifan, Jiang, Shanshan, Yu, Xiaojin, Huang, Sicong, Lan, Ziting, Yu, Yarong, Zhang, Xiaohui, Chen, Jin, Zhang, Jiayin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585557/
https://www.ncbi.nlm.nih.gov/pubmed/37869313
http://dx.doi.org/10.21037/qims-23-233
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author Hu, Yifan
Jiang, Shanshan
Yu, Xiaojin
Huang, Sicong
Lan, Ziting
Yu, Yarong
Zhang, Xiaohui
Chen, Jin
Zhang, Jiayin
author_facet Hu, Yifan
Jiang, Shanshan
Yu, Xiaojin
Huang, Sicong
Lan, Ziting
Yu, Yarong
Zhang, Xiaohui
Chen, Jin
Zhang, Jiayin
author_sort Hu, Yifan
collection PubMed
description BACKGROUND: Epicardial adipose tissue (EAT) is a key aspect in the investigation of cardiac pathophysiology. We sought to develop a deep learning (DL) model for fully automatic extraction and quantification of EAT through pulmonary computed tomography venography (PCTV) images. METHODS: In this retrospective study, we included 128 patients with atrial fibrillation and PCTV from 2 hospitals. A DL model for automated EAT segmentation was developed from a training set of 51 patients and a validation set of 13 patients from hospital A. The algorithm was further validated using an internal test set of 16 patients from hospital A and an external test set of 48 patients from hospital B. The consistency and measurement agreement of EAT quantification were compared between the DL model and the conventional manual protocol using the Dice score coefficient (DSC), Hausdorff distance (HD95), Pearson correlation coefficient, and Bland-Altman plot. RESULTS: In the internal and external test set, automated segmentation with DL was successful in all cases. The total analysis time was shorter for DL than for manual reconstruction (5.43±2.52 vs. 106.20±15.90 min; P<0.001). The EAT segmented with the DL model had good consistency with manual segmentation (the DSC of the internal and external test sets were 0.92±0.02 and 0.88±0.03, respectively). The quantification of EAT evaluated with the 2 methods showed excellent correlation (all correlation coefficients >0.9; all P values <0.001) and minimal measurement difference. CONCLUSIONS: The proposed DL model achieved fully automatic quantification of EAT from PCTV images. The yielded results were highly consistent with those of manual quantification.
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spelling pubmed-105855572023-10-20 Automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography using nnU-Net Hu, Yifan Jiang, Shanshan Yu, Xiaojin Huang, Sicong Lan, Ziting Yu, Yarong Zhang, Xiaohui Chen, Jin Zhang, Jiayin Quant Imaging Med Surg Original Article BACKGROUND: Epicardial adipose tissue (EAT) is a key aspect in the investigation of cardiac pathophysiology. We sought to develop a deep learning (DL) model for fully automatic extraction and quantification of EAT through pulmonary computed tomography venography (PCTV) images. METHODS: In this retrospective study, we included 128 patients with atrial fibrillation and PCTV from 2 hospitals. A DL model for automated EAT segmentation was developed from a training set of 51 patients and a validation set of 13 patients from hospital A. The algorithm was further validated using an internal test set of 16 patients from hospital A and an external test set of 48 patients from hospital B. The consistency and measurement agreement of EAT quantification were compared between the DL model and the conventional manual protocol using the Dice score coefficient (DSC), Hausdorff distance (HD95), Pearson correlation coefficient, and Bland-Altman plot. RESULTS: In the internal and external test set, automated segmentation with DL was successful in all cases. The total analysis time was shorter for DL than for manual reconstruction (5.43±2.52 vs. 106.20±15.90 min; P<0.001). The EAT segmented with the DL model had good consistency with manual segmentation (the DSC of the internal and external test sets were 0.92±0.02 and 0.88±0.03, respectively). The quantification of EAT evaluated with the 2 methods showed excellent correlation (all correlation coefficients >0.9; all P values <0.001) and minimal measurement difference. CONCLUSIONS: The proposed DL model achieved fully automatic quantification of EAT from PCTV images. The yielded results were highly consistent with those of manual quantification. AME Publishing Company 2023-08-16 2023-10-01 /pmc/articles/PMC10585557/ /pubmed/37869313 http://dx.doi.org/10.21037/qims-23-233 Text en 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Hu, Yifan
Jiang, Shanshan
Yu, Xiaojin
Huang, Sicong
Lan, Ziting
Yu, Yarong
Zhang, Xiaohui
Chen, Jin
Zhang, Jiayin
Automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography using nnU-Net
title Automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography using nnU-Net
title_full Automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography using nnU-Net
title_fullStr Automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography using nnU-Net
title_full_unstemmed Automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography using nnU-Net
title_short Automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography using nnU-Net
title_sort automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography using nnu-net
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585557/
https://www.ncbi.nlm.nih.gov/pubmed/37869313
http://dx.doi.org/10.21037/qims-23-233
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