A Graph-Based Method for PET Image Segmentation in Radiotherapy Planning: A Pilot Study

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 development of an accurate and fast method for semi-automatic segmentation of metabolic regions on PET images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Validation was first performed on phantoms containing spheres and irregular inserts of different and known volumes, then tumors from a patient with head and neck cancer were segmented to discuss the clinical applicability of this algorithm. Experimental results show that the segmentation algorithm is accurate and fast and meets the physician requirements in a radiotherapy environment.