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Accurate delineation of the intraprostatic gross tumour volume (GTV) is aprerequisite 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 delineationunderlies interobserver heterogeneity and is time consuming. The aim of thisstudy was to develop a convolutional neural network (CNN) for automatedsegmentation of intraprostatic tumour (GTV-CNN) in PSMA-PET.  Methods: The CNN (3D U-Net) was trained on [68Ga]PSMA-PET images of 152patients from two different institutions and the training labels were generatedmanually using a validated technique. The CNN was tested on two independentinternal (cohort 1: [68Ga]PSMA-PET, n=18 and cohort 2: [18F]PSMA-PET, n=19) andone external (cohort 3: [68Ga]PSMA-PET, n=20) test-datasets. Accordance betweenmanual contours and GTV-CNN was assessed with Dice-S{\o}rensen coefficient(DSC). Sensitivity and specificity were calculated for the two internaltest-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 andspecificities for GTV-CNN were comparable with manual expert contours: 0.98 and0.76 (cohort 1) and 1 and 0.57 (cohort 2), respectively. Computation time wasaround 6 seconds for a standard dataset.  Conclusion: The application of a CNN for automated contouring ofintraprostatic GTV in [68Ga]PSMA- and [18F]PSMA-PET images resulted in a highconcordance with expert contours and in high sensitivities and specificities incomparison with histology reference. This robust, accurate and fast techniquemay be implemented for treatment concepts in primary PCa. The trained model andthe study's source code are available in an open source repository.

Convolutional neural network based deep-learning architecture for  intraprostatic tumour contouring on PSMA PET images in patients with primary  prostate cancer