Target volume delineation of Positron Emission Tomography (PET) images in radiation treatment planning is challenging because of the low spatial resolution and high noise level in PET data. The aim of this work is the devel- opment of an accurate and fast method for semi-automatic segmentation of me- tabolic regions on PET images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Va- lidation was first performed on phantoms containing spheres and irregular in- serts of different and known volumes, then tumors from a patient with head and neck cancer were segmented to discuss the clinical applicability of this algo- rithm. Experimental results show that the segmentation algorithm is accurate and fast and meets the physician requirements in a radiotherapy environment.
Stefano, A., Vitabile, S., Russo, G., Ippolito, M., Sardina, D., Sabini, M., et al. (2013). A Graph-Based Method for PET Image Segmentation in Radiotherapy Planning: A Pilot Study. In A. Petrosino (a cura di), Image Analysis and Processing – ICIAP 2013 (pp. 711-720). Berlin : Springer [10.1007/978-3-642-41184-7_72].
A Graph-Based Method for PET Image Segmentation in Radiotherapy Planning: A Pilot Study
STEFANO, Alessandro;VITABILE, Salvatore;
2013-01-01
Abstract
Target volume delineation of Positron Emission Tomography (PET) images in radiation treatment planning is challenging because of the low spatial resolution and high noise level in PET data. The aim of this work is the devel- opment of an accurate and fast method for semi-automatic segmentation of me- tabolic regions on PET images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Va- lidation was first performed on phantoms containing spheres and irregular in- serts of different and known volumes, then tumors from a patient with head and neck cancer were segmented to discuss the clinical applicability of this algo- rithm. Experimental results show that the segmentation algorithm is accurate and fast and meets the physician requirements in a radiotherapy environment.
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