Abstract
Cardiac magnetic resonance imaging improves on diagnosis of cardiovascular diseases by providing images at high spatiotemporal resolution. Manual evaluation of these time-series, however, is expensive and prone to biased and non-reproducible outcomes. In this paper, we present a method that addresses named limitations by integrating segmentation and disease classification into a fully automatic processing pipeline. We use an ensemble of UNet inspired architectures for segmentation of cardiac structures such as the left and right ventricular cavity (LVC, RVC) and the left ventricular myocardium (LVM) on each time instance of the cardiac cycle. For the classification task, information is extracted from the segmented time-series in form of comprehensive features handcrafted to reflect diagnostic clinical procedures. Based on these features we train an ensemble of heavily regularized multilayer perceptrons (MLP) and a random forest classifier to predict the pathologic target class. We evaluated our method on the ACDC dataset (4 pathology groups, 1 healthy group) and achieve dice scores of 0.945 (LVC), 0.908 (RVC) and 0.905 (LVM) in a cross-validation over the training set (100 cases) and 0.950 (LVC), 0.923 (RVC) and 0.911 (LVM) on the test set (50 cases). We report a classification accuracy of \(94 \%\) on a training set cross-validation and \(92\%\) on the test set. Our results underpin the potential of machine learning methods for accurate, fast and reproducible segmentation and computer-assisted diagnosis (CAD).
Keywords
- Automated cardiac diagnosis challenge
- Cardiac magnetic resonance imaging
- Disease prediction
- Deep learning
- CNN
F. Isensee and P. F. Jaeger—Contributed equally.
This is a preview of subscription content, access via your institution.
Buying options
References
Cohn, J.N., Ferrari, R., Sharpe, N.: Cardiac remodeling-concepts and clinical implications: a consensus paper from an international forum on cardiac remodeling. JACC 35, 569–582 (2000)
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A., van Ginneken, B., Sánchez, C.I.: A Survey on Deep Learning in Medical Image Analysis. arXiv preprint arXiv:1702.05747 (2017)
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_28
Zotti, C., Luo, Z., Lalande, A., Humbert, O., Jodoin, P.-M.: Novel Deep Convolution Neural Network Applied to MRI Cardiac Segmentation. arXiv preprint arXiv:1705.08943 (2017)
Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, M., Caballero, M., Guerrero, R., Cook, S., de Marvao, A., O’Regan, D., et al.: Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation. arXiv preprint arXiv:1705.08302 (2017)
Tran, P.V.: A Fully Convolutional Neural Network for Cardiac Segmentation in Short-axis MRI. arXiv preprint arXiv:1604.00494 (2016)
Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. CMIG 31(4), 198–211 (2008)
Medrano-Gracia, P., Cowan, B.R., Ambale-Venkatesh, B., Bluemke, D.A., Eng, J., Finn, J.P., Fonseca, C.G., Lima, J.A., Suinesiaputra, A., Young, A.A.: Left ventricular shape variation in asymptomatic populations: the multi-ethnic study of atherosclerosis. JCMR 16(1), 56 (2014)
Zhang, X., Ambale-Venkatesh, B., Bluemke, D.A., Cowan, B.R., Finn, J.P., Kadish, A.H., Lee, D.C., Lima, J.A.C., Hundley, W.G., Suinesiaputra, A., Young, A.A., Medrano-Gracia, P.: Information maximizing component analysis of left ventricular remodeling due to myocardial infarction. JTM 13(1), 343 (2015)
Automated Cardiac Diagnosis Challenge. https://www.creatis.insa-lyon.fr/Challenge/acdc. Accessed 23 Jun 2017
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016, Part II. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Kayalibay, B., Jensen, G., van der Smagt, P.: CNN-Based Segmentation of Medical Imaging Data. arXiv preprint arXiv:1701.03056 (2017)
Acknowledgements
The author Sandy Engelhardt was funded by the German Research Foundation (DFG) as part of project B01, SFB/TRR 125 Cognition-Guided Surgery.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Isensee, F., Jaeger, P.F., Full, P.M., Wolf, I., Engelhardt, S., Maier-Hein, K.H. (2018). Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features. In: , et al. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017. Lecture Notes in Computer Science(), vol 10663. Springer, Cham. https://doi.org/10.1007/978-3-319-75541-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-75541-0_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75540-3
Online ISBN: 978-3-319-75541-0
eBook Packages: Computer ScienceComputer Science (R0)