Abstract
The software-assisted planning of radiofrequency-ablation of liver tumors calls for robust and fast methods to segment the tumor and surrounding vascular structures from clinical data to allow a numerical estimation, whether a complete thermal destruction of the tumor is feasible taking the cooling effect of the vessels into account. As the clinical workflow in radiofrequency-ablation does not allow for time consuming planning procedures, the implementation of robust and fast segmentation algorithms is critical in building a streamlined software application tailored to the clinical needs. To suppress typical artifacts in clinical CT or MRT data - like inhomogeneous background density due to the imaging procedure - a Bayesian background compensation is developed, which subsequently allows a robust segmentation of the vessels by fast threshold based algorithms. The presented Bayesian background compensation has proven to handle a wide range of image perturbances in MRT and CT data and leads to a fast and reliable identification of vascular structures in clinical data.
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References
Arnold JB, Liow JS, Schaper KA, et al. Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects. Neuroimage 2001;13:931–943.
Brinkmann BH, Manduca A, Robb RA. Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction. IEEE TransMed Imaging 1998;17:161–171.
Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998;17:87–97.
Shattuck DW, Sandor-Leahy SR, Schaper KA, et al. Magnetic resonance image tissue classification using a partial volume model. Neuroimage 2001;13:856–876.
Fischer R, Hanson KM, V Dose V, von Der Linden W. Background estimation in experimental spectra. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics 2000 Feb;61(2):1152–60 2000;61:1152–1160.
Guglielmetti F, Fischer R, Dose V. Mixture modeling for background and sources separation in x-ray astronomical images. American Institut of Physics; 2004. 111–118.
Schumaker LL, Utreras FI. On generalized cross validation for tensor smoothing splines. SIAM J Sci Stat Comput 1990;11(4):713–731.
Bornemann L, Kuhnigk JM, Dicken V, et al. OncoTREAT-A software assistant for oncological therapy monitoring. Procs CARS 2005; 429–434.
Weihusen A, Ritter F, Pereira P, et al. Towards a workflow-oriented software assistance for the radiofrequency ablation. Lecture Notes in Informatics 2006;93:507–513.
Kröger T, Altrogge I, Preusser T, et al. Numerical simulation of radio frequency ablation with state dependent material parameters in three space dimensions. LNCS 2006;4191:380–388.
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© 2007 Springer-Verlag Berlin Heidelberg
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Zidowitz, S. et al. (2007). Bayesian Vessel Extraction for Planning of Radiofrequency-Ablation. In: Horsch, A., Deserno, T.M., Handels, H., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2007. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71091-2_38
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DOI: https://doi.org/10.1007/978-3-540-71091-2_38
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