Objectives: This study aimed to develop and evaluate MUSeg-PSV, a real-time deep-learning framework for segmentation of the prostate gland and seminal vesicles on 29-MHz micro-ultrasound (MicroUS), intended to support anatomical orientation during MicroUS-guided prostate biopsy and local staging of prostate cancer. Patients and methods: MicroUS data from 14 patients (21 full-length videos; 2588 annotated frames) were used to train and test MUSeg-PSV, a lightweight single-stage convolutional model based on YOLOv11s-seg. Two training strategies were compared: a standard configuration with basic augmentations and an augmentation-rich configuration incorporating advanced spatial and photometric transformations. Model performance was assessed on a held-out test set using mean average precision (mAP), dice similarity coefficient (DSC), expected calibration error (ECE) and Brier score. Real-time feasibility was evaluated through inference latency and qualitative assessment of temporal consistency on unseen videos. Results: The augmentation-rich model outperformed the standard configuration, yielding a 21% relative increase in mAP and a 4.6-fold improvement in seminal vesicle AP (0.183 → 0.842). Prostate DSC increased from 0.675 to 0.770 ( Conclusion: MUSeg-PSV enables reliable, real-time multiclass segmentation of high-frequency MicroUS, providing stable delineation of the prostate and seminal vesicles at clinically viable frame rates. These results support the potential of AI-assisted MicroUS to enhance operator orientation during MicroUS-guided prostate biopsy and local staging, and to promote standardised identification of seminal vesicles within prostate cancer diagnostic and staging workflows.