Abstract
The treatment of metastatic brain tumors with stereotactic radiosurgery requires that the clinician first locate the tumors and measure their volumes. Thoroughly searching a patient scan for brain tumors and delineating the lesions can be a long and difficult task when done manually and is also prone to human error. In this paper, we present an automated method for detecting changes in brain tumor lesions over longitudinal scans to aide the clinician’s task of determining tumor volumes. Our approach jointly registers the current image with a previous scan while estimating changes in intensity correspondences due to tumor growth or regression. We combine the label map with correspondence changes with tumor segmentations from a previous scan to estimate the metastases in the new image. Alignment and tumor tracking results show promise on 28 registrations using real patient data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Maher, E.A., Mietz, J., Arteaga, C.L., DePinho, R.A., Mohla, S.: Brain metastasis: Opportunities in basic and translational research. Cancer Research 69, 6015–6020 (2009)
Mehta, M.P., Tsao, M.N., Whelan, T.J., Morri, D.E., Hayman, J.A., Flickinger, J.C., Mills, M., Rogers, C.L., Souhami, L.: The american society for therapeutic radiology and oncology (astro) evidence-based review of the role of radiosurgery for brain metastases. International Journal of Radiation Oncology Biology Physics 63, 37–46 (2005)
Rey, D., Subsol, G., Delingette, H., Ayache, N.: Automatic detection and segmentation of evolving processes in 3d medical images: Application to multiple sclerosis. Medical Image Analysis 6, 163–179 (2002)
Rouchdy, Y., Bloch, I.: A chance-constrained programming level set method for longitudinal segmentation of lung tumors in ct. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 3407–3410 (2011)
Xu, J., Greenspan, H., Napel, S., Rubin, D.L.: Automated temporal tracking and segmentation of lymphoma on serial ct examinations. Medical Physics 38(11), 5879–5886 (2011)
Corso, J.J., Sharon, E., Dube, S., El-Saden, S., Sinha, U., Yuille, A.: Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Trans. Med. Imaging 27, 629–640 (2008)
Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Medical Image Analysis 8, 275–283 (2004)
Zacharaki, E.I., Hogea, C.S., Shen, D., Biros, G., Davatzikos, C.: Non-diffeomorphic registration of brain tumor images by simulating tissue loss and tumor growth. Neuroimage 46(3), 762–774 (2009)
Chitphakdithai, N., Duncan, J.S.: Non-rigid Registration with Missing Correspondences in Preoperative and Postresection Brain Images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 367–374. Springer, Heidelberg (2010)
Celeux, G., Govaert, G.: A classification em algorithm for clustering and two stochastic versions. Comput. Statist. Data Anal. 14, 315–332 (1992)
Meng, X.-L., Rubin, D.B.: Maximum likelihood estimation via the ecm algorithm: A general framework. Biometrika 80(2), 267–278 (1993)
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast mr images. IEEE Trans. Med. Imaging 18, 712–721 (1999)
Celeux, G., Forbes, F., Peyrard, N.: Em procedures using mean field-like approximations for markov model-based image segmentation. Pattern Recognition 36, 131–144 (2003)
Papademetris, X., Jackowski, M., Rajeevan, N., Okuda, H., Constable, R., Staib, L.: BioImage Suite: An integrated medical image analysis suite. Section of Bioimaging Sciences, Dept. of Diagnostic Radiology, Yale School of Medicine, http://www.bioimagesuite.org
Jackowski, A.P., Papademetris, X., Klaiman, C., Win, L., Pober, B., Schultz, R.T.: A non-linear intensity-based brain morphometric analysis of williams syndrome. Human Brain Mapping (2004)
Meadows, G.G. (ed.): Integration/Interaction of Oncologic Growth. Cancer Growth and Progression, vol. 15. Springer (2005)
Weltens, C., Menten, J., Feron, M., Bellon, E., Demaerel, P., Maes, F., van den Bogaert, W., van der Schueren, E.: Interobserver variations in gross tumor volume delineation of brain tumors on computed tomography and impact of magnetic resonance imaging. Radiotherapy and Oncology 60, 49–59 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chitphakdithai, N., Chiang, V.L., Duncan, J.S. (2012). Tracking Metastatic Brain Tumors in Longitudinal Scans via Joint Image Registration and Labeling. In: Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M. (eds) Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data. STIA 2012. Lecture Notes in Computer Science, vol 7570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33555-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-33555-6_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33554-9
Online ISBN: 978-3-642-33555-6
eBook Packages: Computer ScienceComputer Science (R0)