Abstract
The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transform between both imaging systems, we employ a discriminative learning based approach to localize the TEE transducer in X-ray images. Instead of time-consuming manual labeling, we generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. An evaluation on more than 1900 images reveals that our approach reduces detection failures by 95% compared to cross validation on the test set and improves the localization error from 1.5 to 0.8 mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Beijbom, O.: Domain adaptation for computer vision applications. Technical report, University of California, San Diego (June 2012)
Gao, G., Penney, G., Ma, Y., Gogin, N., Cathier, P., Arujuna, A., Morton, G., Caulfield, D., Gill, J., Rinaldi, C.A., Hancock, J., Redwood, S., Thomas, M., Razavi, R., Gijsbers, G., Rhode, K.: Registration of 3D trans-esophageal echocardiography to X-ray fluoroscopy using image-based probe tracking. Med. Image Anal. 16, 38–49 (2012)
Jain, A., Gutierrez, L., Stanton, D.: 3D TEE registration with X-ray fluoroscopy for interventional cardiac applications. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 321–329. Springer, Heidelberg (2009)
Lang, P., Seslija, P., Chu, M.W.A., Bainbridge, D., Guiraudon, G.M., Jones, D.L., Peters, T.M.: US - fluoroscopy registration for transcatheter aortic valve implantation. IEEE Trans. Biomed. Eng. 59(5), 1444–1453 (2012)
Margolis, A.: A literature review of domain adaptation with unlabeled data. Technical report, University of Washington (2011)
Mountney, P., Ionasec, R., Kaiser, M., Mamaghani, S., Wu, W., Chen, T., John, M., Boese, J., Comaniciu, D.: Ultrasound and fluoroscopic images fusion by autonomous ultrasound probe detection. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 544–551. Springer, Heidelberg (2012)
Shimodaira, H.: Improving predictive inference under covariate shift by weighting the log-likelihood function. J. Statistical Planning and Inference 90, 227–244 (2000)
Sugiyama, M., Suzuki, T., Kanamori, T.: Density ratio estimation: A comprehensive review. In: Proc. Workshop on Statistical Experiment and Its Related Topics, Kyoto, Japan, pp. 10–31 (March 2010)
Tu, Z.: Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering. In: Proc. ICCV, vol. 2, pp. 1589–1596 (October 2005)
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Med. Imaging 27(11), 1668–1681 (2008)
Zhu, X.: Semi-supervised learning literature survey. Technical Report 1530, University of Wisconsin-Madison (July 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Heimann, T., Mountney, P., John, M., Ionasec, R. (2013). Learning without Labeling: Domain Adaptation for Ultrasound Transducer Localization. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-40760-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40759-8
Online ISBN: 978-3-642-40760-4
eBook Packages: Computer ScienceComputer Science (R0)