Target volume segmentation of PET images by an iterative method based on threshold value

Abstract Objectives An automatic segmentation method is presented for PET images based on an iterative approximation by threshold value that includes the influence of both lesion size and background present during the acquisition. Material and methods Optimal threshold values that represent a correct segmentation of volumes were determined based on a PET phantom study that contained different sizes of spheres and different known radiation environments. These optimal values were normalized to background and adjusted by regression techniques to a two-variable function: lesion volume and signal-to-background ratio (SBR). This adjustment function was used to build an iterative segmentation method. Based on this method, a procedure of automatic delineation was proposed. This procedure was validated on phantom images and its viability was confirmed by retrospectively applying it on two oncology patients. Results The resulting adjustment function obtained had a linear dependence with the SBR and was inversely proportional and negative with the volume. During the validation of the proposed method, it was found that the volume deviations with respect to its real value and CT volume were below 10% and 9%, respectively, except for lesions with a volume below 0.6 ml. Conclusions The automatic segmentation method proposed can be applied in clinical practice to tumor radiotherapy treatment planning in a simple and reliable way with a precision close to the resolution of PET images.