参考文献/References:
]
[1] Massalha S,Clarkin O,Thornhill R,et al. Decision support tools, systems, and artificial intelligence in cardiac imaging [J]. Can J Cardiol,2018,34:827-838.
[2] Chartrand G,Cheng PM,Vorontsov E,et al. Deep learning:a primer for radiologists[J]. Radiographics,2017,37:2113-2131.
[3] Garcia EV,Klein JL,Taylor AT. Clinical decision support systems in myocardial perfusion imaging[J]. J Nucl Cardiol ,2014,21:427-439.
[4] Jiang J,Trundle P,Ren J,et al. Medical imaging analysis with artificial neural networks[J]. Comput Med Imaging Graph ,2010,34:617-631.
[5] Dilsizian SE,Siegel EL. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment[J]. Curr Cardiol Rep ,2014,16:441.
[6] Peng P,Lekadir K,Gooya A,et al. A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging[J]. MAGMA,2016,29:155-195.
[7] Margeta J,Schaeffter T,Tobon-Gomez C,et al. Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets [J].IEEE Trans Med Imaging,2015,34:1460-1473.
[8] Shang L,Lv JC,Yi Z. Rigid medical image registration using PCA neural network[J]. Neurocomputing,2006,69:1717-1722.
[9] Jaiswal RR, Gaikwad AN. Neural network assisted effective lossy compression of medical images[J]. Iete Tech Rev ,2006,23:119-126.
[10] Suzuki K,Abe H,MacMahon H,et al . Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)[J]. IEEE Trans Med Imaging,2006,25:406-416.
[11] Hainc L,Kukal J. Role of robust processing in ANN denoising of 2D image[J]. Neural Network World,2006,16:163-176.
[12] Meftah B,Lezoray O,Benyettou A. Segmentation and edge detection based on spiking neural network model[J]. Neural Process Lett,2010,32:131-146.
[13] Kim Y,Chung Y,Hyeon Y. Biomedical signal processing and control automatic localization of anatomical landmarks in cardiac MR perfusion using random forests[J]. Biomed Signal Process Control,2017,38:370-378.
[14] Kurkure U,Pednekar A,Muthupillai R,et al . Localization and segmentation of left ventricle in cardiac cine-MR images[J]. IEEE Trans Biomed Eng,2009,56:1360-1370.
[15] Grosgeorge D,Petitjean C,Caudron J,et al . Automatic cardiac ventricle segmentation in MR images:a validation study [J]. Int J Comput Assist Radiol Surg, 2011,6:573-581.
[16] Angelie E,Koning PJH,Danilouchkine MG,et al. Optimizing the automatic segmentation of the left ventricle in magnetic resonance images[J].Med Phys,2005,32(2):369-375.
[17] Yang F,He Y,Hussain M,et al. Convolutional neural network for the detection of end-diastole and end-systole frames in free-breathing cardiac magnetic resonance imaging[J]. Comput Math Methods Med ,2017,2017:1640835.
[18] Haddad F,Doyle R,Murphy DJ,et al . Right ventricular function in cardiovascular disease, part Ⅱ. Pathophysiology,clinical importance,and management of right ventricular failure [J]. Circulation,2008,117:1717-1731.
[19] Sanz J, Conroy J, Narula J. Imaging of the right ventricle[J]. Cardiol Clin ,2012,30:189-203.
[20] Vitarelli A,Terzano C. Do we have two hearts? N ew insights in right ventricular function supported by myocardial imaging echocardiography[J]. Heart Fail Rev,2010,15:39-61.
[21] Petitjean C,Zuluaga MA,Bai W,et al. Right ventricle segmentation from cardiac MRI:a collation study [J]. Med Image Anal,2015,19:187-202.
[22] Petitjean C,Dacher JN. A review of segmentation methods in short axis cardiac MR images[J]. Med Image Anal,2011,15:169-184.
[23] Grosgeorge D,Petitjean C,Dacher JN,et al . Graph cut segmentation with a statistical shape model in cardiac MRI[J]. Comput Vis Image Underst,2013,117:1027-1035.
[24] Ringenberg J, Deo M, Devabhaktuni V, et al. Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI[J]. Comput Med Imaging Graph , 2014,38:190-201.
[25] Avendi MR,Kheradvar A,Jafarkhani H. Automatic segmentation of the right ventricle from cardiac MRI using a learining-based approach[J]. Magn Reson Med ,2017,86(6):2439-2448.
[26] Tao Q,Yan W,Wang Y,et al. Deep learning-based method for fully automatic quantification of left ventricular function from Cine MR images:a multivendor, multicenter study [J]. Radiology,2019,290(1):81-88.
[27] Greenwood JP,Motwani M,Maredia N,et al. Comparison of cardiovascular magnetic resonance and single-photon emission computed tomography in women with suspected coronary artery disease from the Clinical Evaluation of Magnetic Resonance Imaging in Coronary Heart Disease(CE-MARC) Trial[J]. Circulation,2014,129(10):1129-1138.
[28] Tarroni G,Corsi C,Antkowiak PF,et al. Myocardial perfusion: near-automated evaluation from contrast-enhanced MR images obtained at rest and during vasodilator stress[J]. Radiology,2012,265(2):576-583.
[29] Goel A,McColl R,King KS,et al . Fully automated tool to identify the aorta and compute flow using phase-contrast MRI:validation and application in a large population based study [J]. J Magn Reson Imaging ,2014,40:221-228.
[30] Karim R,Housden RJ,Balasubramaniam M,et al. Evaluation of current algorithms for segmentation of scar tissue from late gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge[J]. J Cardiovasc Magn Reson ,2013,15:105.
[31] Karim R,Bhagirath P,Claus P,et al. Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images[J]. Med Image Anal,2016,30:95-107.
[32] Slomka PJ,Dey D,Sitek A,et al. Cardiac imaging:working towards fully-automated machine analysis & interpretation[J]. Expert Rev Med Devices ,2017,14(3):197-212.
[33] Zhang N,Yang G,Gao ZF,et al. Deep learning for diagnosis of chronic myocardial infarction on nonehanced cardiac Cine MRI [J]. Radiology,2019,291(3):606-617.
[34] Betancur J, Commandeur F, Motlagh M. Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT,a multicenter study [J]. JACC Cardiovasc Imaging ,2018,11 (11):1654-1663.
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