Abstract
BACKGROUND: Positron emission tomography (PET) with a [(18)F]fluoroethyl)-L-tyrosine ([(18)F]FET) tracer is of growing importance in the management of glioblastoma for the estimation of tumor extent and extraction of diagnostic and prognostic parameters. Robust and accurate glioblastoma segmentation methods are essential to maximize the benefits of this imaging modality. Given the importance of setting the foreground threshold during manual tumor delineation, this study investigates the added value of incorporating such prior knowledge to guide the automated segmentation and improve performance. Two segmentation networks were trained based on the nnU-Net guidelines: one with the [(18)F]FET PET image as sole input, and one with an additional input channel for the threshold map. For the latter, we investigate the benefit of manually obtained thresholds and explore automated prediction and generation of such maps. A fully automated pipeline was constructed by selecting the best performing threshold prediction approach and cascading this with the tumor segmentation model. RESULTS: The proposed two-channel network shows increased performance with guidance of threshold maps originating from the same reader whose ground-truth tumor label the prediction is compared to (DSC = 0.901). When threshold maps were generated by a different reader, performance reverted to levels comparable to the one-channel network and inter-reader variability. The proposed full pipeline achieves results on par with current state of the art (DSC = 0.807). CONCLUSIONS: Incorporating a threshold map can significantly improve tumor segmentation performance when it aligns well with the ground-truth label. However, the current inability to reliably reproduce these maps-both manually and automatically-or the ground-truth tumor labels, restricts the achievable accuracy for automated glioblastoma segmentation on [(18)F]FET PET, highlighting the need for more consistent definitions of such ground-truth delineations.