18F-FDG PET images segmentation using morphological watershed: a phantom study

Segmentation of ¹⁸F-FDG PET images could be helpful for delineation of tumor volume in radiotherapy and patient follow-up. The most commonly implemented method on clinical workstations is maximum intensity thresholding, which is inappropriate for heterogeneous uptakes. Our aim was to develop and evaluate a more sophisticated segmentation method, based on the morphological watershed. We developed a segmentation method taking into account PET images characteristics. We evaluated it first on phantom images, using an integrated PET/CT unit and taking CT images as reference images. To simulate tumors in a background activity, we used 6 homogeneous spheres of various volumes in a cylindrical phantom and 3 heterogeneous cylinders in an anthropomorphic phantom. The quality of segmentation was evaluated in terms of volume, shape and position. We compared the results with a maximum intensity threshold segmentation method fitting the volume, taken as reference segmentation. A quantitation analysis completed the phantom study. For both phantom acquisitions, the segmentation obtained with the watershed based algorithm gave satisfying results with the index integrating volume, shape and position. Results considering this index were not significantly different from the reference segmentation (p > 0.5). Errors of volume recovery reached 18% for watershed segmentation. The quantitation analysis on phantoms highlighted partial volume effect, with an error of activity concentration measurement on segmented images ranging between 42% and 51%. Performances of the watershed method evaluated in this study were comparable with an optimized segmentation on phantom images. The quantitation recovery of PET regions with this method was similar with to other segmentation methods.