Abstract
Image quality assessment (IQA) is crucial in large-scale population imaging so that high-throughput image analysis can extract meaningful imaging biomarkers at scale. Specifically, in this paper, we address a seemingly basic yet unmet need: the automatic detection of missing (apical and basal) slices in Cardiac Magnetic Resonance Imaging (CMRI) scans, which is currently performed by tedious visual assessment. We cast the problem as classification tasks, where the bottom and top slices are tested for the presence of typical basal and apical patterns. Inspired by the success of deep learning methods, we train Convolutional Neural Networks (CNN) to construct a set of discriminative features. We evaluated our approach on a subset of the UK Biobank datasets. Precision and Recall figures for detecting missing apical slice (MAS) (81.61 % and 88.73 %) and missing basal slice (MBS) (74.10 % and 88.75 %) are superior to other state-of-the-art deep learning architectures. Cross-dataset experiments show the generalization ability of our approach.
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References
Attili, A.K., Schuster, A., Nagel, E., Reiber, J.H., van der Geest, R.J.: Quantification in cardiac MRI: advances in image acquisition and processing. Int. J. Cardiovasc. Imaging 26(1), 27–40 (2010)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Bowl, K.: Data science bowl cardiac challenge data. https://www.kaggle.com/c/second-annual-data-science-bowl/data, Accessed 17 Mar 2016
Chen, X., Xu, Y., Yan, S., Wong, D.W.K., Wong, T.Y., Liu, J.: Automatic feature learning for glaucoma detection based on deep learning. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 669–677. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24574-4_80
Ferreira, P.F., Gatehouse, P.D., Mohiaddin, R.H., Firmin, D.N.: Cardiovascular magnetic resonance artefacts. J. Cardiovasc. Magn. Reson. 15(1), 41 (2013)
van der Graaf, A., Bhagirath, P., Ghoerbien, S., Götte, M.: Cardiac magnetic resonance imaging: artefacts for clinicians. Neth. Heart J. 22(12), 542–549 (2014)
Klinke, V., Muzzarelli, S., Lauriers, N., Locca, D., Vincenti, G., Monney, P., Lu, C., Nothnagel, D., Pilz, G., Lombardi, M., et al.: Quality assessment of cardiovascular magnetic resonance in the setting of the european CMR registry: description and validation of standardized criteria. J. Cardiovasc. Magn. Reson. 15, 55 (2013)
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)
Krupa, K., Bekiesińska-Figatowska, M.: Artifacts in magnetic resonance imaging. Pol. J. Radiol. 80, 93 (2015)
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 609–616. ACM (2009)
Oskoei, M.A., Hu, H.: A Survey on Edge Detection Methods. University of Essex, UK (2010)
Petersen, S.E., Matthews, P.M., Bamberg, F., Bluemke, D.A., Francis, J.M., Friedrich, M.G., Leeson, P., Nagel, E., Plein, S., Rademakers, F.E., et al.: Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of uk biobank-rationale, challenges and approaches. J. Cardiovasc. Magn. Reson. 15(1), 46 (2013)
Petersen, S.E., Matthews, P.M., Francis, J.M., Robson, M.D., Zemrak, F., Boubertakh, R., Young, A.A., Hudson, S., Weale, P., Garratt, S., et al.: UK biobanks cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18(1), 1 (2016)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Cogn. Model. 5(3), 1 (1988)
Saad, M.A., Bovik, A.C., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans. Image Process. 21(8), 3339–3352 (2012)
Wang, Z., Wu, G., Sheikh, H.R., Simoncelli, E.P., Yang, E.H., Bovik, A.C.: Quality-aware images. IEEE Trans. Image Process. 15(6), 1680–1689 (2006)
Xue, W., Mou, X., Zhang, L., Bovik, A.C., Feng, X.: Blind image quality assessment using joint statistics of gradient magnitude and laplacian features. IEEE Trans. Image Process. 23(11), 4850–4862 (2014)
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Zhang, L. et al. (2016). Automated Quality Assessment of Cardiac MR Images Using Convolutional Neural Networks. In: Tsaftaris, S., Gooya, A., Frangi, A., Prince, J. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2016. Lecture Notes in Computer Science(), vol 9968. Springer, Cham. https://doi.org/10.1007/978-3-319-46630-9_14
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DOI: https://doi.org/10.1007/978-3-319-46630-9_14
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