Details

Autor(en) / Beteiligte

• Zidowitz, Stephan
• Drexl, Johann
• Kröger, Tim
• Preusser, Tobias
• Ritter, Felix
• Weihusen, Andreas
• Peitgen, Heinz-Otto

Titel

Bayesian Vessel Extraction for Planning of Radiofrequency-Ablation

Ist Teil von

• Bildverarbeitung für die Medizin 2007, p.187-191

Ort / Verlag

Berlin, Heidelberg: Springer Berlin Heidelberg

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.