A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans

Rosito, 2008, Pericardial fat, visceral abdominal fat, cardiovascular disease risk factors, and vascular calcification in a community-based sample: the Framingham Heart Study, Circulation, 117, 605, 10.1161/CIRCULATIONAHA.107.743062 Kortelainen, 2002, Myocardial infarction and coronary pathology in severely obese people examined at autopsy, Int. J. Obes. Relat. Metab. Disord., 26, 73, 10.1038/sj.ijo.0801852 Iacobellis, 2005, Adiponectin expression in human epicardial adipose tissue in vivo is lower in patients with coronary artery disease, Cytokine, 29, 251 Ding, 2008, The association of pericardial fat with calcified coronary plaque, Obesity, 16, 1914, 10.1038/oby.2008.278 Dey, 2010, Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and metabolic syndrome, Atherosclerosis, 209, 136, 10.1016/j.atherosclerosis.2009.08.032 Mookadam, 2010, Epicardial fat and its association with cardiovascular risk: a cross-sectional observational study, Heart Views, 11, 103, 10.4103/1995-705X.76801 Sicari, 2011, Pericardial rather than epicardial fat is a cardiometabolic risk marker: an MRI vs echo study, J. Am. Soc. Echocardiogr., 24, 1156, 10.1016/j.echo.2011.06.013 Geyer, 2015, State of the art: iterative CT reconstruction techniques, Radiology, 276, 339, 10.1148/radiol.2015132766 Boas, 2012, CT artifacts: causes and reduction techniques, Imaging Med., 4, 229, 10.2217/iim.12.13 Ueno, 2009, Increased epicardial fat volume quantified by 64-multidetector computed tomography is associated with coronary atherosclerosis and totally occlusive lesions, Circ, J, 73, 1927 Sarin, 2008, Clinical significance of epicardial fat measured using cardiac multislice computed tomography, Am. J. Cardiol., 102, 767, 10.1016/j.amjcard.2008.04.058 Rodrigues, 2017, Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes, Comput. Biol. Med., 89, 520, 10.1016/j.compbiomed.2017.02.010 La Grutta, 2016, Quantification of epicardial adipose tissue in coronary calcium score and CT coronaryangiography image data sets: comparison of attenuation values, thickness and volumes, Br. J. Radiol., 89, 20150773, 10.1259/bjr.20150773 Milanese, 2019, Quantification of epicardial fat with cardiac CT angiography and association with cardiovascular risk factors in symptomatic patients: from the ALTER-BIO (Alternative Cardiovascular Bio-Imaging markers) registry, Diagn. Interv. Radiol., 25, 35, 10.5152/dir.2018.18037 D'Errico, 2017, Quantitative analysis of epicardial fat volume: effects of scanning protocol and reproducibility of measurements in non-contrast cardiac CT vs. coronary CT angiography, Quant, Imaging Med. Surg., 7, 326, 10.21037/qims.2017.06.08 Ding X, 2016, Automated pericardial fat quantification from coronary magnetic resonance angiography: feasibility study, J. Med. Imaging, 3, 014002, 10.1117/1.JMI.3.1.014002 Cristobal-Huerta, 2015, Automated quantification of epicardial adipose tissue in cardiac magnetic resonance imaging, Proc. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 7308 Teme T, 2014, Quantification of epicardial fat volume using cardiovascular magnetic resonance imaging, J. Cardiovasc. Magn. Reson., 16, O112, 10.1186/1532-429X-16-S1-O112 T. Rohlfing, R. Brandt, R. Menzel, D.B. Russakoff, C.R. Maurer, Quo vadis, atlas-based segmentation?, In Handbook of Biomedical Image Analysis, pp.435–486. Topics in Biomedical Engineering International Book Series. Springer, Boston, MA, USA. doi: 10.1007/0-306-48608-3_11. Antonelli, 2019, GAS: a genetic atlas selection strategy in multi-atlas segmentation framework, Med. Image Anal., 52, 97, 10.1016/j.media.2018.11.007 Liang, 2015, Construction of brain atlases based on a multi-center MRI dataset of 2020 Chinese adults, Sci. Rep., 5, 18216, 10.1038/srep18216 Xiong, 2019, Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network, IEEE Trans. Med. Imaging, 38, 515, 10.1109/TMI.2018.2866845 Li, 2019, A novel end-to-end brain tumor segmentation method using improved fully convolutional networks, Comput. Biol. Med., 108, 150, 10.1016/j.compbiomed.2019.03.014 Tang, 2010, The construction of a Chinese MRI brain atlas: a morphometric comparison study between Chinese and Caucasian cohorts, Neuroimage, 51, 33, 10.1016/j.neuroimage.2010.01.111 Bai, 2012, Population differences in brain morphology and microstructure among Chinese, Malay, and Indian neonates, PLoS One, 7 Takahashi, 2011, Gender and age differences in normal adult human brain: voxel-based morphometric study, Hum. Brain Mapp., 32, 1050, 10.1002/hbm.21088 Allen, 2019, A road map for translational research on artificial intelligence in medical imaging: from the 2018 National Institutes Research of Health/RSNA/ACR/The Academy Workshop, J. Am. Coll. Radiol., 1 Assunção, 2016, Cardiac magnetic resonance imaging and computed tomography in ischemic cardiomyopathy: an update, Radiol. Bras., 49, 26, 10.1590/0100-3984.2014.0055 Fischbach, 2009, Recommendations of the Heart Diagnosis Working Group of the German Roentgen Society for use of computerized tomography and magnetic resonance tomography in heart diagnosis. 1 – computerized tomography, Fortschr. Röntgenstr., 181, 700, 10.1055/s-0028-1109533 Norlén, 2016, Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography, J. Med. Imaging, 3, 034003, 10.1117/1.JMI.3.3.034003 Rodrigues, 2016, A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography, Comput. Methods Progr. Biomed., 123, 109, 10.1016/j.cmpb.2015.09.017 Shahzad, 2013, Automatic quantification of epicardial fat volume on non-enhanced cardiac CT scans using a multi-atlas segmentation approach, Med. Phys., 40, 10.1118/1.4817577 Al'Aref, 2019, Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging, Eur. Heart J., 40, 1975, 10.1093/eurheartj/ehy404 Zhuang, 2019, Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge, Med. Image Anal., 10.1016/j.media.2019.101537 Zlokolica, 2017, Semiautomatic epicardial fat segmentation based on fuzzy c-means clustering and geometric ellipse fitting, J. Healthc. Eng., 2017, 5817970, 10.1155/2017/5817970 Militello, 2015, A fully automatic 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation, Comput. Biol. Med., 62, 277, 10.1016/j.compbiomed.2015.04.030 Coppini, 2010, Quantification of epicardial fat by cardiac CT imaging, Open Med, Inform J., 4, 126 Yang, 2018, Generalizing deep models for ultrasound image segmentation, 497 Rundo, 2019, USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets, Neurocomputing, 365, 31, 10.1016/j.neucom.2019.07.006 Boykov, 2000, Interactive organ segmentation using graph cuts, proc. Medical image computing and computer-assisted intervention (MICCAI), vol. 1935 Rundo, 2018, GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model, Nat. Comput., 17, 521, 10.1007/s11047-017-9636-z Hamamci, 2012, Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications, IEEE Trans. Med. Imaging, 31, 790, 10.1109/TMI.2011.2181857 Yoshizumi, 1999, Abdominal fat: standardized technique for measurement at CT, Radiology, 211, 283, 10.1148/radiology.211.1.r99ap15283 Boas, 2012, CT artifacts: causes and reduction techniques, Imaging Med., 4, 229, 10.2217/iim.12.13 Villa, 2016, Coronary artery anomalies overview: the normal and the abnormal, World, J. Radiol., 8, 537 Rickards, 1998, Genetic history of the population of Sicily, Hum. Biol., 70, 699 Sarno, 2017, Ancient and recent admixture layers in Sicily and Southern Italy trace multiple migration routes along the Mediterranean, Sci. Rep., 7, 1984, 10.1038/s41598-017-01802-4 Tapiovaara, 1985, SNR and DQE analysis of broad spectrum X-ray imaging, Phys. Med. Biol., 30, 519, 10.1088/0031-9155/30/6/002 Firbank, 1999, A comparison of two methods for measuring the signal to noise ratio on MR images, Phys. Med. Biol., 44, N261, 10.1088/0031-9155/44/12/403 Taha, 2015, Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool, BMC Med. Imaging, 15, 29, 10.1186/s12880-015-0068-x Cabitza, 2017, Unintended consequences of machine learning in medicine, J. Am. Med. Assoc., 318, 517, 10.1001/jama.2017.7797 Gambino, 2019, A framework for data-driven adaptive GUI generation based on DICOM, J. Biomed. Inform., 88, 37, 10.1016/j.jbi.2018.10.009 Cheng, 2010, Pericardial fat burden on ECG-gated noncontrast CT in asymptomatic patients who subsequently experience adverse cardiovascular events, JACC Cardiovasc. Imaging, 3, 352, 10.1016/j.jcmg.2009.12.013 Wong, 2011, Pericardial fat is associated with atrial fibrillation severity and ablation outcome, J. Am. Coll. Cardiol., 57, 1745, 10.1016/j.jacc.2010.11.045 Hatem, 2014, Epicardial adipose tissue and atrial fibrillation, Cardiovasc. Res., 102, 205, 10.1093/cvr/cvu045 Mazurek, 2014, Relation of proinflammatory activity of epicardial adipose tissue to the occurrence of atrial fibrillation, Am. J. Cardiol., 113, 1505, 10.1016/j.amjcard.2014.02.005 De Vos, 2017, ConvNet-based localization of anatomical structures in 3-D medical images, IEEE Trans. Med. Imaging, 36, 1470, 10.1109/TMI.2017.2673121 Singh, 2018, Machine learning in cardiac CT: basic concepts and contemporary data, J. Cardiovasc. Comput. Tomogr., 12, 192, 10.1016/j.jcct.2018.04.010