Objective To observe the value of deep learning model for automatically segmenting prostate on large-field T2WI based on integrated 68Ga-prostate specific membrane antigen(PSMA)PET/MRI.Methods Ninety male patients with prostate tumors who underwent 68Ga-PSMA PET/MRI were retrospectively enrolled and divided into training set(n=72)and validation set(n=18)at the ratio of 4∶1.Models were established based on 3D SegResNet and 3D Unet deep learning neural networks,respectively.Taken physicians'manual segmentation results as reference standards,the performances of models for segmenting the peripheral zone(PZ)and central zone(CZ)+transition zone(TZ)of prostate on large-field T2WI were evaluated.Results In both training and validation sets,the Dice similarity coefficient(DSC)of 3D SegResNet deep learning model for segmenting prostate on T 2WI were both higher than that of 3D Unet model(both P＜0.05),the 95％Hausdorff distance(HD95)of SegR esNet deep learning model for segmenting prostate CZ+TZ was lower than that of 3D Unet model(both P＜0.05),while DSC and HD95 of these 2 models for segmenting prostate CZ+TZ were superior to PZ(all P＜0.05).Conclusion 3D SegResNet deep learning model could be used to automatically segment prostate on large-field T2WI based on integrated 68Ga-PSMA PET/MRI.