  Accurate delineation of the intraprostatic gross tumour volume (GTV) is a
prerequisite for treatment approaches in patients with primary prostate cancer
(PCa). Prostate-specific membrane antigen positron emission tomography
(PSMA-PET) may outperform MRI in GTV detection. However, visual GTV delineation
underlies interobserver heterogeneity and is time consuming. The aim of this
study was to develop a convolutional neural network (CNN) for automated
segmentation of intraprostatic tumour (GTV-CNN) in PSMA-PET.
  Methods: The CNN (3D U-Net) was trained on [68Ga]PSMA-PET images of 152
patients from two different institutions and the training labels were generated
manually using a validated technique. The CNN was tested on two independent
internal (cohort 1: [68Ga]PSMA-PET, n=18 and cohort 2: [18F]PSMA-PET, n=19) and
one external (cohort 3: [68Ga]PSMA-PET, n=20) test-datasets. Accordance between
manual contours and GTV-CNN was assessed with Dice-S{\o}rensen coefficient
(DSC). Sensitivity and specificity were calculated for the two internal
test-datasets by using whole-mount histology.
  Results: Median DSCs for cohorts 1-3 were 0.84 (range: 0.32-0.95), 0.81
(range: 0.28-0.93) and 0.83 (range: 0.32-0.93), respectively. Sensitivities and
specificities for GTV-CNN were comparable with manual expert contours: 0.98 and
0.76 (cohort 1) and 1 and 0.57 (cohort 2), respectively. Computation time was
around 6 seconds for a standard dataset.
  Conclusion: The application of a CNN for automated contouring of
intraprostatic GTV in [68Ga]PSMA- and [18F]PSMA-PET images resulted in a high
concordance with expert contours and in high sensitivities and specificities in
comparison with histology reference. This robust, accurate and fast technique
may be implemented for treatment concepts in primary PCa. The trained model and
the study's source code are available in an open source repository.
