Top

Published in:

2019 | OriginalPaper | Chapter

Calculation of Anatomical and Functional Metrics Using Deep Learning in Cardiac MRI: Comparison Between Direct and Segmentation-Based Estimation

Authors : Hao Xu, Jurgen E. Schneider, Vicente Grau

Published in: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In this paper we propose a collection of left ventricle (LV) quantification methods using different versions of a common neural network architecture. In particular, we compare the accuracy obtained with direct calculation (regression) of the desired metrics, a segmentation network and a novel combined approach. We also introduce temporal dynamics through the use of a Long Short-Term Memory (LSTM) network. We train and evaluate our methods on MICCAI 2018 Left Ventricle Full Quantification Challenge dataset. We perform 5-fold cross-validation on the training dataset and compare our results with the state-of-the-art methods evaluated on the same dataset. In our experiments, segmentation-based methods outperform all the other variants as well as current state of the art. The introduction of LSTM does produces only minor improvements in accuracy. The novel segmentation/estimation network improves the results on estimation-only but does not reach the accuracy of segmentation-based metric calculation.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Convexity and Connectivity Principles Applied for Left Ventricle Segmentation and Quantification

next chapter Automated Full Quantification of Left Ventricle with Deep Neural Networks

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef

Karamitsos, T.D., Francis, J.M., Myerson, S., Selvanayagam, J.B., Neubauer, S.: The role of cardiovascular magnetic resonance imaging in heart failure. J. Am. Coll. Cardiol. 54(15), 1407–1424 (2009)CrossRef

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE CVPR, pp. 3431–3440 (2015)

Oktay, O., et al.: Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE TMI 37(2), 384–395 (2018)

Poudel, R.P.K., Lamata, P., Montana, G.: Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation. In: Zuluaga, M.A., Bhatia, K., Kainz, B., Moghari, M.H., Pace, D.F. (eds.) RAMBO/HVSMR 2016. LNCS, vol. 10129, pp. 83–94. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52280-7_8CrossRef

Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRef

Suinesiaputra, A., et al.: Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours. JCMR 17(1), 63 (2015)

Tran, P.V.: A fully convolutional neural network for cardiac segmentation in short-axis MRI (2016). arXiv preprint arXiv:1604.00494

Vigneault, D.M., Xie, W., Ho, C.Y., Bluemke, D.A., Noble, J.A.: \(\Omega \)-Net (Omega-Net): fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks. Med. image Anal. 48, 95–106 (2018)CrossRef

10.

Xue, W., Islam, A., Bhaduri, M., Li, S.: Direct multitype cardiac indices estimation via joint representation and regression learning. IEEE TMI 36(10), 2057–2067 (2017)

11.

Xue, W., Lum, A., Mercado, A., Landis, M., Warrington, J., Li, S.: Full quantification of left ventricle via deep multitask learning network respecting intra- and inter-task relatedness. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017, Part III. LNCS, vol. 10435, pp. 276–284. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_32CrossRef

12.

Xue, W., Nachum, I.B., Pandey, S., Warrington, J., Leung, S., Li, S.: Direct estimation of regional wall thicknesses via residual recurrent neural network. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 505–516. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_40CrossRef

13.

Xue, W., Brahm, G., Pandey, S., Leung, S., Li, S.: Full left ventricle quantification via deep multitask relationships learning. MIA 43, 54–65 (2018)

14.

Zeiler, M.D.: ADADELTA: an adaptive learning rate method (2012). arXiv preprint arXiv:1212.5701

Title: Calculation of Anatomical and Functional Metrics Using Deep Learning in Cardiac MRI: Comparison Between Direct and Segmentation-Based Estimation
Authors: Hao Xu
Jurgen E. Schneider
Vicente Grau
Publisher: Springer International Publishing
Book: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges
Print ISBN: 978-3-030-12028-3

Electronic ISBN: 978-3-030-12029-0

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-12029-0_43

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner