  Accurate segmentation of prostate tumours from PET images presents a
formidable challenge in medical image analysis. Despite considerable work and
improvement in delineating organs from CT and MR modalities, the existing
standards do not transfer well and produce quality results in PET related
tasks. Particularly, contemporary methods fail to accurately consider the
intensity-based scaling applied by the physicians during manual annotation of
tumour contours. In this paper, we observe that the prostate-localised uptake
threshold ranges are beneficial for suppressing outliers. Therefore, we utilize
the intensity threshold values, to implement a new custom-feature-clipping
normalisation technique. We evaluate multiple, established U-Net variants under
different normalisation schemes, using the nnU-Net framework. All models were
trained and tested on multiple datasets, obtained with two radioactive tracers:
[68-Ga]Ga-PSMA-11 and [18-F]PSMA-1007. Our results show that the U-Net models
achieve much better performance when the PET scans are preprocessed with our
novel clipping technique.
