Abstract
3D ultrasound (US) is a promising technique to perform automatic extraction of standard planes for fetal anatomy assessment. This requires prior organ localization, which is difficult to obtain with direct learning approaches because of the high variability in fetus size and orientation in US volumes. In this paper, we propose a methodology to overcome this spatial variability issue by scaling and automatically aligning volumes in a common 3D reference coordinate system. This preprocessing allows the organ detection algorithm to learn features that only encodes the anatomical variability while discarding the fetus pose. All steps of the approach are evaluated on 126 manually annotated volumes, with an overall mean localization error of 11.9 mm, showing the feasibility of multi-organ detection in 3D fetal US with machine learning.
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References
Dong, N., Xin, Y., Xin, C., Chien-Ting, C., Siping, C., Pheng Ann, H., Shengli, L., Jing, Q., Tianfu, W.: Standard plane localization in ultrasound by radial component model and selective search. Ultrasound Med. Biol. 40, 2728–2742 (2014)
Chen, H., Dou, Q., Ni, D., Cheng, J.-Z., Qin, J., Li, S., Heng, P.-A.: Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 507–514. Springer, Cham (2015). doi:10.1007/978-3-319-24553-9_62
Yaqub, M., Kelly, B., Papageorghiou, A.T., Noble, J.A.: Guided random forests for identification of key fetal anatomy and image categorization in ultrasound scans. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 687–694. Springer, Cham (2015). doi:10.1007/978-3-319-24574-4_82
Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends in Computer Graphics and Vision (2012)
Cuingnet, R., Prevost, R., Lesage, D., Cohen, L.D., Mory, B., Ardon, R.: Automatic detection and segmentation of kidneys in 3D CT images using random forests. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 66–74. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33454-2_9
Gauriau, R., Cuingnet, R., Lesage, D., Bloch, I.: Multi-organ localization with cascaded global-to-local regression and shape prior. Med. Image Anal. 23, 70–83 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural International Proceedings Systems (NIPS) (2012)
Papageorghiou, A.T., et al.: for the Internat. Fetal, for the 21st Cent., N.G.C.: Inter-nat. standards for fetal growth based on serial ultrasound measurements: the fetal growth longitudinal study of the INTERGROWTH-21st project. The Lancet (2014)
Acknowledgements
This work was done in Philips Research Paris (MediSys), with images acquired and manually annotated at the John Radcliffe Hospital, Oxford, in collaboration with the University of Oxford, with funding from Philips Ultrasound.
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Raynaud, C. et al. (2017). Multi-organ Detection in 3D Fetal Ultrasound with Machine Learning. In: Cardoso, M., et al. Fetal, Infant and Ophthalmic Medical Image Analysis. OMIA FIFI 2017 2017. Lecture Notes in Computer Science(), vol 10554. Springer, Cham. https://doi.org/10.1007/978-3-319-67561-9_7
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DOI: https://doi.org/10.1007/978-3-319-67561-9_7
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