A. H. Abdi, C. Luong, T. Tsang, G. Allan, S. Nouranian et al., , p.572

D. , Automatic quality assessment of echocardiograms using 573, 2017.

, Robust estimation of carotid artery wall motion using the elasticity-based 606 state-space approach, Medical Image Analysis, vol.37, pp.1-21

B. Georgescu and X. S. Zhou, Database-guided segmentation of 608 anatomical structures with complex appearance, pp.429-436, 2005.

A. Graves, Supervised sequence labelling, in: Supervised sequence 610 labelling with recurrent neural networks, pp.5-13, 2012.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image 612 recognition, pp.770-778, 2016.

K. He and X. Zhang, Identity mappings in deep residual networks, vol.614, pp.630-645, 2016.

G. Jacob, J. A. Noble, C. Behrenbruch, A. D. Kelion, and A. P. Banning, A shape-space-based approach to tracking myocardial borders and 617 quantifying regional left-ventricular function applied in echocardiography, vol.616, 2002.

, IEEE Transactions on Medical Imaging, vol.21, pp.226-238

M. Jaderberg and K. Simonyan, Spatial transformer networks, 620 Advances in neural information processing systems, pp.2017-2025, 2015.

R. M. Lang, L. P. Badano, V. Mor-avi, J. Afilalo, A. Armstrong et al., , p.622

L. Flachskampf and F. A. , Recommendations for cardiac chamber 623 quantification by echocardiography in adults: an update from the american 624 society of echocardiography and the european association of cardiovascular 625 imaging, Journal of the American Society of Echocardiography, vol.28, pp.1-39, 2015.

R. M. Lang, M. Bierig, R. B. Devereux, F. A. Flachskampf, and E. Foster, , p.627

P. A. Pellikka and M. H. Picard, Recommendations for chamber 628 quantification, European Journal of Echocardiography, vol.7, pp.79-108, 2006.

S. Lathuilière and R. Juge, Deep mixture of linear inverse 630 regressions applied to head-pose estimation, pp.4817-4828, 2017.

G. Luo, S. Dong, K. Wang, W. Zuo, S. Cao et al., Multi-views 632 fusion cnn for left ventricular volumes estimation on cardiac mr images, 2018.

, IEEE Transactions on Biomedical Engineering, vol.65, pp.1924-1934

R. Malladi, J. A. Sethian, and B. C. Vemuri, Shape modeling with front 635 propagation: A level set approach, IEEE Transactions on Pattern Analysis 636 and Machine Intelligence, vol.17, pp.158-175, 1995.

Y. Mo, F. Liu, D. Mcilwraith, G. Yang, J. Zhang et al., , 2018.

, The deep poincaré map: A novel approach for left ventricle segmentation, vol.639, pp.561-568

J. C. Nascimento, Robust shape tracking with multiple models in 641 ultrasound images, IEEE Transactions on Image Processing, vol.17, pp.392-406, 2008.

O. Oktay, E. Ferrante, K. Kamnitsas, M. Heinrich, W. Bai et al., , p.643

J. Cook, S. A. De-marvao, and A. , Anatomically constrained 644 neural networks (acnns): application to cardiac image enhancement and 645 segmentation, IEEE Transactions on Medical Imaging, vol.37, pp.384-395, 2018.

N. Paragios, A level set approach for shape-driven segmentation and 647 tracking of the left ventricle, IEEE Transactions on Medical Imaging, vol.22, pp.773-776, 2003.

M. Pascual, D. Pascual, F. Soria, T. Vicente, A. Hernandez et al., , p.650

M. Valdes, Effects of isolated obesity on systolic and diastolic left 651 ventricular function, Heart, vol.89, pp.1152-1156, 2003.

P. Peng, K. Lekadir, and A. Gooya, A review of heart chamber 653 segmentation for structural and functional analysis using cardiac magnetic 654 resonance imaging, Magnetic Resonance Materials in Physics, vol.29, pp.155-195, 2016.

S. Pereira, Enhancing interpretability of automatically extracted 656 machine learning features: application to a rbm-random forest system on 657 brain lesion segmentation, Medical image analysis, vol.44, pp.228-244, 2018.

D. Rav?, C. Wong, and F. Deligianni, Deep learning for health 659 informatics, IEEE Journal of Biomedical and Health Informatics, vol.21, pp.4-21, 2017.

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks 661 for biomedical image segmentation, pp.234-241, 2015.

N. B. Schiller, P. M. Shah, M. Crawford, A. Demaria, and R. Devereux, , p.663

H. Feigenbaum, H. Gutgesell, and N. Reichek, Recommendations 664 for quantitation of the left ventricle by two-dimensional echocardiography, 1989.

C. Szegedy and S. Ioffe, Inception-v4, inception-resnet and the 667 impact of residual connections on learning, pp.4278-4284, 2017.

D. M. Vigneault, W. Xie, and C. Y. Ho, ?-net (omega-net): Fully 669 automatic, multi-view cardiac mr detection, orientation, and segmentation 670 with deep neural networks, Medical Image Analysis, vol.48, pp.95-106, 2018.

Z. Wang, M. B. Salah, B. Gu, A. Islam, A. Goela et al., Direct 672 estimation of cardiac biventricular volumes with an adapted bayesian 673 formulation, IEEE Transactions on Biomedical Engineering, vol.61, pp.1251-1260, 2014.

L. Wu, J. Z. Cheng, S. Li, B. Lei, T. Wang et al., Fuiqa: Fetal 675 ultrasound image quality assessment with deep convolutional networks, IEEE Transactions on Cybernetics, vol.676, pp.1336-1349, 2017.

S. Xingjian, Z. Chen, H. Wang, and D. Y. Yeung, Convolutional 678 lstm network: A machine learning approach for precipitation nowcasting, Advances in neural information processing systems, vol.679, pp.802-810, 2015.

C. Xu, L. Xu, Z. Gao, S. Zhao, H. Zhang et al., Direct 681 delineation of myocardial infarction without contrast agents using a joint 682 motion feature learning architecture, Medical image analysis, vol.50, pp.82-94, 2018.

W. Xue and G. Brahm, Full left ventricle quantification via deep 684 multitask relationships learning, Medical Image Analysis, vol.43, pp.54-65, 2018.

W. Xue, A. Islam, M. Bhaduri, and S. Li, Direct multitype cardiac 686 indices estimation via joint representation and regression learning, IEEE 687 Transactions on Medical Imaging, vol.36, pp.2057-2067, 2017.

W. Xue, A. Lum, A. Mercado, and M. Landis, Full quantification 689 of left ventricle via deep multitask learning network respecting intra-and 690 inter-task relatedness, pp.276-284, 2017.

W. Xue, I. B. Nachum, S. Pandey, J. Warrington, S. Leung et al., , 2017.

, Direct estimation of regional wall thicknesses via residual recurrent neural 693 network, pp.505-516

L. Yu, X. Yang, H. Chen, J. Qin, and P. A. Heng, Volumetric convnets 695 with mixed residual connections for automated prostate segmentation from 696 3d mr images, pp.66-72, 2017.

X. Zhen, A. Islam, M. Bhaduri, I. Chan, and S. Li, Direct and 698 simultaneous four-chamber volume estimation by multi-output regression, vol.699, pp.669-676, 2015.

X. Zhen, Z. Wang, A. Islam, M. Bhaduri, I. Chan et al., Multi-701 scale deep networks and regression forests for direct bi-ventricular volume 702 estimation, Medical Image Analysis, vol.30, pp.120-129, 2016.

X. Zhen, Z. Wang, A. Islam, I. Chan, and S. Li, A comparative study 704 of methods for cardiac ventricular volume estimation, Annual Meeting-705, 2014.

, Radiological Society of North America (RSNA), pp.228-244

X. Zhen, Z. Wang, M. Yu, and S. Li, Supervised descriptor learning 707 for multi-output regression, Proceedings of the IEEE conference on 708 computer vision and pattern recognition, pp.1211-1218, 2015.

X. Zhen and Z. Wang, Direct estimation of cardiac bi-ventricular 710 volumes with regression forests, pp.586-593, 2014.

X. Zhen, M. Yu, X. He, and S. Li, Multi-target regression via robust 712 low-rank learning, IEEE transactions, vol.40, pp.497-504, 2017.