-
1
-
-
84969930014
-
Non-invasive cardiac imaging: Past, present and future
-
Selvanayagam JB. Non-Invasive Cardiac Imaging: Past, Present and Future. Heart, lung & circulation. 2016;25 (8): 755-6.
-
(2016)
Heart, Lung & Circulation.
, vol.25
, Issue.8
, pp. 755-756
-
-
Selvanayagam, J.B.1
-
3
-
-
84924127094
-
Left ventricle: Fully automated segmentation based on spatiotemporal continuity and myocardium information in cine cardiac magnetic resonance imaging (LV-FAST)
-
Wang LJ, Pei MC, Codella NCF, Kochar M, Weinsaft JW, Li JQ, et al. Left Ventricle: Fully Automated Segmentation Based on Spatiotemporal Continuity and Myocardium Information in Cine Cardiac Magnetic Resonance Imaging (LV-FAST). Biomed Res Int. 2015.
-
(2015)
Biomed Res Int.
-
-
Wang, L.J.1
Pei, M.C.2
Codella, N.C.F.3
Kochar, M.4
Weinsaft, J.W.5
Li, J.Q.6
-
5
-
-
85016124927
-
A combined multi-scale deep learning and random forests approach for direct left ventricular volumes estimation in 3D echocardiography
-
Dong SY, Luo GN, Sun GX, Wang KQ, Zhang HG. A combined multi-scale deep learning and random forests approach for direct left ventricular volumes estimation in 3D echocardiography. Computing in Cardiology: IEEE; 2016.
-
(2016)
Computing in Cardiology: IEEE
-
-
Dong, S.Y.1
Luo, G.N.2
Sun, G.X.3
Wang, K.Q.4
Zhang, H.G.5
-
6
-
-
84919361099
-
Right ventricle segmentation from cardiac MRI: A collation study
-
Petitjean C, Zuluaga MA, Bai WJ, Dacher JN, Grosgeorge D, Caudron J, et al. Right ventricle segmentation from cardiac MRI: A collation study. Medical Image Analysis. 2015;19 (1): 187-202.
-
(2015)
Medical Image Analysis.
, vol.19
, Issue.1
, pp. 187-202
-
-
Petitjean, C.1
Zuluaga, M.A.2
Bai, W.J.3
Dacher, J.N.4
Grosgeorge, D.5
Caudron, J.6
-
7
-
-
84964325589
-
Right ventricular assessment at cardiac MRI: Initial clinical experience utilizing an ISSENSE reconstruction
-
Bogachkov A, Ayache JB, Allen BD, Murphy I, Carr ML, Spottiswoode B, et al. Right ventricular assessment at cardiac MRI: initial clinical experience utilizing an ISSENSE reconstruction. The international journal of cardiovascular imaging. 2016;32 (7): 1081-91.
-
(2016)
The International Journal of Cardiovascular Imaging.
, vol.32
, Issue.7
, pp. 1081-1091
-
-
Bogachkov, A.1
Ayache, J.B.2
Allen, B.D.3
Murphy, I.4
Carr, M.L.5
Spottiswoode, B.6
-
8
-
-
84863501936
-
Cardiac MRI assessment of right ventricular function in acquired heart disease: Factors of variability
-
Caudron J, Fares J, Lefebvre V, Vivier PH, Petitjean C, Dacher JN. Cardiac MRI Assessment of Right Ventricular Function in Acquired Heart Disease: Factors of Variability. Acad Radiol. 2012;19 (8): 991-1002.
-
(2012)
Acad Radiol.
, vol.19
, Issue.8
, pp. 991-1002
-
-
Caudron, J.1
Fares, J.2
Lefebvre, V.3
Vivier, P.H.4
Petitjean, C.5
Dacher, J.N.6
-
9
-
-
84938394939
-
Big heart data: Advancing health informatics through data sharing in cardiovascular imaging
-
Suinesiaputra A, Medrano-Gracia P, Cowan BR, Young AA. Big Heart Data: Advancing Health Informatics Through Data Sharing in Cardiovascular Imaging. Ieee J Biomed Health. 2015;19 (4): 1283-90.
-
(2015)
Ieee J Biomed Health.
, vol.19
, Issue.4
, pp. 1283-1290
-
-
Suinesiaputra, A.1
Medrano-Gracia, P.2
Cowan, B.R.3
Young, A.A.4
-
10
-
-
84893929553
-
Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI
-
Ringenberg J, Deo M, Devabhaktuni V, Berenfeld O, Boyers P, Gold J. Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI. Comput Med Imag Grap. 2014;38 (3): 190-201.
-
(2014)
Comput Med Imag Grap.
, vol.38
, Issue.3
, pp. 190-201
-
-
Ringenberg, J.1
Deo, M.2
Devabhaktuni, V.3
Berenfeld, O.4
Boyers, P.5
Gold, J.6
-
13
-
-
84969962996
-
Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning
-
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Trans Med Imaging. 2016;35 (5): 1285-98.
-
(2016)
IEEE Trans Med Imaging.
, vol.35
, Issue.5
, pp. 1285-1298
-
-
Shin, H.C.1
Roth, H.R.2
Gao, M.3
Lu, L.4
Xu, Z.5
Nogues, I.6
-
15
-
-
84958981335
-
Multiscale deep networks and regression forests for direct biventricular volume estimation
-
Zhen X, Wang Z, Islam A, Bhaduri M, Chan I, Li S. Multiscale deep networks and regression forests for direct biventricular volume estimation. Med Image Anal. 2016;30: 120-9.
-
(2016)
Med Image Anal.
, vol.30
, pp. 120-129
-
-
Zhen, X.1
Wang, Z.2
Islam, A.3
Bhaduri, M.4
Chan, I.5
Li, S.6
-
17
-
-
85019805716
-
Fully automatic segmentation of heart chambers in cardiac MRI using deep learning
-
MR Avendi, Arash Kheradvar, Jafarkhani H. Fully automatic segmentation of heart chambers in cardiac MRI using deep learning. J Cardiovasc Magn R. 2016;18 (Suppl1): P351.
-
(2016)
J Cardiovasc Magn R.
, vol.18
, pp. P351
-
-
Avendi, M.R.1
Kheradvar, A.2
Jafarkhani, H.3
-
18
-
-
79851510761
-
A review of segmentation methods in short axis cardiac MR images
-
Petitjean C, Dacher JN. A review of segmentation methods in short axis cardiac MR images. Medical Image Analysis. 2011;15 (2): 169-84.
-
(2011)
Medical Image Analysis.
, vol.15
, Issue.2
, pp. 169-184
-
-
Petitjean, C.1
Dacher, J.N.2
-
19
-
-
84955240327
-
A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging
-
Peng P, Lekadir K, Gooya A, Shao L, Petersen SE, Frangi AF. A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magma. 2016;29 (2): 155-95.
-
(2016)
Magma.
, vol.29
, Issue.2
, pp. 155-195
-
-
Peng, P.1
Lekadir, K.2
Gooya, A.3
Shao, L.4
Petersen, S.E.5
Frangi, A.F.6
-
20
-
-
84937874239
-
Deep joint task learning for generic object extraction
-
Wang X, Zhang L, Lin L, Liang Z, Zuo W, editors. Deep joint task learning for generic object extraction. Advances in Neural Information Processing Systems; 2014.
-
(2014)
Advances in Neural Information Processing Systems
-
-
Wang, X.1
Zhang, L.2
Lin, L.3
Liang, Z.4
Zuo, W.5
|