Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases

Best, 2016, Long-term survival of individuals born with congenital heart disease: a systematic review and meta-analysis, J Am Heart Assoc, 5, 002846, 10.1161/JAHA.115.002846

Oster, 2013, Temporal trends in survival among infants with critical congenital heart defects, Pediatrics, 131, 1502, 10.1542/peds.2012-3435

Yuan, 2002, Contrast-enhanced high resolution mri for atherosclerotic carotid artery tissue characterization, J Magn Reson Imag, 15, 62, 10.1002/jmri.10030

Lima, 2004, Cardiovascular magnetic resonance imaging: current and emerging applications, J Am Coll Cardiol, 44, 1164, 10.1016/j.jacc.2004.06.033

Avendi, 2016, A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac mri, Med Image Anal, 30, 108, 10.1016/j.media.2016.01.005

Avendi, 2017, Automatic segmentation of the right ventricle from cardiac mri using a learning-based approach, Magn Reson Med, 78, 2439, 10.1002/mrm.26631

Bai, 2018, Automated cardiovascular magnetic resonance image analysis with fully convolutional networks, J Cardiovasc Magn Reson, 20, 65, 10.1186/s12968-018-0471-x

Backhaus, 2019, Fully automated quantification of biventricular volumes and function in cardiovascular magnetic resonance: applicability to clinical routine settings, J Cardiovasc Magn Reson, 21, 24, 10.1186/s12968-019-0532-9

Arafati, 2019, Artificial intelligence in pediatric and adult congenital cardiac mri: an unmet clinical need, Cardiovascular diagnosis and therapy, 9, 310, 10.21037/cdt.2019.06.09

The 2015 Kaggle Second Annual Data Science Bowl. httpp://http://www.kaggle.com/c/second-annual-data-science-bowl(2015)

Petersen, 2015, Uk biobank's cardiovascular magnetic resonance protocol, J Cardiovasc Magn Reson, 18, 8, 10.1186/s12968-016-0227-4

Petitjean, 2011, A review of segmentation methods in short axis cardiac mr images, Med Image Anal, 15, 169, 10.1016/j.media.2010.12.004

Petitjean, 2015, Right ventricle segmentation from cardiac mri: a collation study, Med Image Anal, 19, 187, 10.1016/j.media.2014.10.004

Litjens, 2017, A survey on deep learning in medical image analysis, Med Image Analy, 42, 60, 10.1016/j.media.2017.07.005

Kazeminia S, Baur C, Kuijper A, van Ginneken B, Navab N, Albarqouni S, Mukhopadhyay A. Gans for medical image analysis. arXiv:1809.06222. https://arxiv.org/abs/1809.06222

Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434. https://arxiv.org/abs/1511.06434

Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Cham: Springer; 2015. p. 234–41.

Tran PV. A fully convolutional neural network for cardiac segmentation in short-axis mri. arXiv:1604.00494. https://arxiv.org/abs/1604.00494

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In:Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

Roth, 2015, Improving computer-aided detection using convolutional neural networks and random view aggregation, IEEE Trans Med Imaging, 35, 1170, 10.1109/TMI.2015.2482920

Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256(2010)

Nesterov, 1983, A method for unconstrained convex minimization problem with the rate of convergence o (1/kˆ2), Doklady AN USSR, 269, 543

Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. In: Advances in Neural Information Processing Systems, pp. 2234–2242 (2016)

Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv:1412.6980. https://arxiv.org/abs/1412.6980

Fisher, R.A., et al.: Statistical methods for research workers. Statistical methods for research workers. (llth ed. revised) (1950)

Koo, 2016, A guideline of selecting and reporting intraclass correlation coefficients for reliability research, Journal of chiropractic medicine, 15, 155, 10.1016/j.jcm.2016.02.012

Tavakoli, 2013, A survey of shaped-based registration and segmentation techniques for cardiac images, Comput Vis Image Underst, 117, 966, 10.1016/j.cviu.2012.11.017

Queirós, 2014, Fast automatic myocardial segmentation in 4d cine cmr datasets, Med Image Analy, 18, 1115, 10.1016/j.media.2014.06.001

Hajiaghayi, M., Groves, E.M., Jafarkhani, H., Kheradvar, A.: A 3-d active contour method for automated segmentation of the left ventricle from magnetic resonance images. In: IEEE Transactions on Biomedical Engineering 64(1), 134–144 (2017)

Dreijer, 2013, Left ventricular segmentation from mri datasets with edge modelling conditional random fields, BMC Med Imaging, 13, 24, 10.1186/1471-2342-13-24

Snaauw G, Gong D, Maicas G, van den Hengel A, Niessen WJ, Verjans J, Carneiro G. End-to-end diagnosis and segmentation learning from cardiac magnetic resonance imaging. In: Proceedings of the IEEE 16th international symposium on biomedical imaging; 2019. p. 802–5.

Yu L, Yang X, Qin J, Heng P-A. 3d fractalnet: dense volumetric segmentation for cardiovascular mri volumes. In: Reconstruction, Segmentation, and Analysis of Medical Images. Springer, Cham;2016. pp. 103–110.

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y.Generative adversarial nets. In: Advances in Neural Information Processing Systems, 2014; pp. 2672–2680

Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems, 2017; pp.6626–6637

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and PatternRecognition, 2015; pp. 1–9 (2015)