Segmentation of epicardial adipose tissue in cardiac MRI using deep learning

Fulton, Miranda R.; Givan, Amy; Fernández-del-Valle, María; Klingensmith, Jon D.

doi:10.1117/12.2550013

Cited by 3 publications

(2 citation statements)

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“…Cristobal-Huerta et al [ 51 ] developed an automatic pipeline composed of Law texture filters, snakes and K-cosine curvature analysis to partially quantify EAT volume, albeit on 10 subjects only. In a semi-automatic processing, Fulton et al [ 52 ] applied landmarks on short-axis images from 12 subjects to unroll images into polar coordinates before employing a neural network for detection of epicardial fat contours. We were unable to compare our results with those previous works as segmentation metrics (e.g., DSC metric or Jaccard similarity index) were not provided.…”

Section: Discussionmentioning

confidence: 99%

Deep-Learning Segmentation of Epicardial Adipose Tissue Using Four-Chamber Cardiac Magnetic Resonance Imaging

et al. 2022

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In magnetic resonance imaging (MRI), epicardial adipose tissue (EAT) overload remains often overlooked due to tedious manual contouring in images. Automated four-chamber EAT area quantification was proposed, leveraging deep-learning segmentation using multi-frame fully convolutional networks (FCN). The investigation involved 100 subjects—comprising healthy, obese, and diabetic patients—who underwent 3T cardiac cine MRI, optimized U-Net and FCN (noted FCNB) were trained on three consecutive cine frames for segmentation of central frame using dice loss. Networks were trained using 4-fold cross-validation (n = 80) and evaluated on an independent dataset (n = 20). Segmentation performances were compared to inter-intra observer bias with dice (DSC) and relative surface error (RSE). Both systole and diastole four-chamber area were correlated with total EAT volume (r = 0.77 and 0.74 respectively). Networks’ performances were equivalent to inter-observers’ bias (EAT: DSCInter = 0.76, DSCU-Net = 0.77, DSCFCNB = 0.76). U-net outperformed (p < 0.0001) FCNB on all metrics. Eventually, proposed multi-frame U-Net provided automated EAT area quantification with a 14.2% precision for the clinically relevant upper three quarters of EAT area range, scaling patients’ risk of EAT overload with 70% accuracy. Exploiting multi-frame U-Net in standard cine provided automated EAT quantification over a wide range of EAT quantities. The method is made available to the community through a FSLeyes plugin.

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Section: Discussionmentioning

confidence: 99%

Deep-Learning Segmentation of Epicardial Adipose Tissue Using Four-Chamber Cardiac Magnetic Resonance Imaging

et al. 2022

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“…This added feature may prove beneficial, as it could allow the assessment of a subclinical cardiovascular risk biomarker from MRI sequences that are already routinely performed for the evaluation of cardiac function in obese patients. Moreover, as an increasing number of methods for automated segmentation of EAT are being proposed showing promising results also on MRI scans [ 24 ], images acquired on open-bore scanners may prove a suitable substrate for future clinical developments.…”

Section: Discussionmentioning

confidence: 99%

Quantification of epicardial adipose tissue in obese patients using an open-bore MR scanner

et al. 2022

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Background Our aim was to evaluate the reproducibility of epicardial adipose tissue (EAT) volume, measured on scans performed using an open-bore magnetic resonance scanner. Methods Consecutive patients referred for bariatric surgery, aged between 18 and 65 years who agreed to undergo cardiac imaging (MRI), were prospectively enrolled. All those with cardiac pathology or contraindications to MRI were excluded. MRI was performed on a 1.0-T open-bore scanner, and EAT was segmented on all scans at both systolic and diastolic phase by two independent readers (R1 with four years of experience and R2 with one year). Data were reported as median and interquartile range; agreement and differences were appraised with Bland-Altman analyses and Wilcoxon tests, respectively. Results Fourteen patients, 11 females (79%) aged 44 (41–50) years, underwent cardiac MRI. For the first and second readings, respectively, EAT volume was 86 (78–95) cm3 and 85 (79–91) cm3 at systole and 82 (74–95) cm3 and 81 (75–94) cm3 at diastole for R1, and 89 (79–99) cm3 and 93 (84–98) cm3 at systole and 92 (85–103) cm3 and 93 (82–94) cm3 at diastole for R2. R1 had the best reproducibility at diastole (bias 0.3 cm3, standard deviation of the differences (SD) 3.3 cm3). R2 had the worst reproducibility at diastole (bias 3.9 cm3, SD 12.1 cm3). The only significant difference between systole and diastole was at the first reading by R1 (p = 0.016). The greatest bias was that of inter-reader reproducibility at diastole (-9.4 cm3). Conclusions Reproducibility was within clinically acceptable limits in most instances.

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Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

Zhang

Sun²,

Jiang

et al. 2021

European J Hybrid Imaging

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Background Artificial intelligence (AI) technology has been increasingly developed and studied in cardiac imaging. This systematic review summarizes the latest progress of image segmentation, quantification, and the clinical application of AI in evaluating cardiac adipose tissue. Methods We exhaustively searched PubMed and the Web of Science for publications prior to 30 April 2021. The search included eligible studies that used AI for image analysis of epicardial adipose tissue (EAT) or pericoronary adipose tissue (PCAT). The risk of bias and concerns regarding applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results Of the 140 initially identified citation records, 19 high-quality studies were eligible for this systematic review, including 15 (79%) on the image segmentation and quantification of EAT or PCAT and 4 (21%) on the clinical application of EAT or PCAT in cardiovascular diseases. All 19 included studies were rated as low risk of bias in terms of flow and timing, reference standards, and the index test and as having low concern of applicability in terms of reference standards and patient selection, but 16 (84%) studies did not conduct external validation. Conclusion AI technology can provide accurate and quicker methods to segment and quantify EAT and PCAT images and shows potential value in the diagnosis and risk prediction of cardiovascular diseases. AI is expected to expand the value of cardiac adipose tissue imaging.

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Segmentation of epicardial adipose tissue in cardiac MRI using deep learning

Cited by 3 publications

References 0 publications

Deep-Learning Segmentation of Epicardial Adipose Tissue Using Four-Chamber Cardiac Magnetic Resonance Imaging

Deep-Learning Segmentation of Epicardial Adipose Tissue Using Four-Chamber Cardiac Magnetic Resonance Imaging

Quantification of epicardial adipose tissue in obese patients using an open-bore MR scanner

Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

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