## 4 Discussion  
The accurate determination of prostate volume is crucial for the effective management of prostate diseases, and ultrasound is commonly used due to its cost-effectiveness and rapid assessment capabilities. Prostate volume can be estimated through US image segmentation and using the Ellipsoid formula. However, clinicians often face challenges in accurately estimating PV due to imaging artifacts that obscure prostate boundaries. To address these issues, automatic methods have been developed to enhance PV determination and reduce patient examination time. This paper reviews recent advancements in applying deep learning (DL) to automatically segment prostate in USs images. Our review of DL architectures for prostate segmentation reveals that the presented methods show comparable performances. However, evaluating these architectures solely based on performance metrics is insufficient, as various factors influence the results. The design of the architecture and characteristics of the dataset, including homogeneity, US image quality, and quantity, play significant roles. Experts should consider dataset quality and its clinical applicability, particularly given the prevalence of low-quality US acquisitions in practice. Public datasets are needed for fair comparisons of DL architectures in prostate segmentation in US. Additionally, standard deviation values should be reported to provide transparency regarding the consistency of results. In most studies, the performance of DL architectures is highlighted by presenting both the best and worst segmentation outcomes. Inaccurate predictions often occur in the apex and base regions due to poor visibility. Consequently, multi-directional image information has been explored, involving view-specific architectures that generate final segmentation outcomes using axial, coronal, and sagittal prostate information. Future research should examine the extent to which multi-directional image input contributes to segmentation performance, considering the increased training workload. For instance, when a 3D reconstruction is based on a certain number of 2D segmentations, it is essential to determine if adding more slices significantly improves model performance to balance input size and accuracy. Post-processing strategies like Multi-Directional Contour Refinement (MDCR) should be carefully evaluated, as they depend on set distance parameters that may yield inconsistent results with diverse image data. While incorporating multi-directional information appears to enhance segmentation performance, it is unclear if [PERSON] et al. 's (2022) [22] design can be applied in clinical practice, as the authors manually cropped each acquisition to a Region of Interest (ROI), potentially excluding image artifacts that typically degrade model performance. Several studies have employed additional shape information to enhance segmentation outcomes, using statistical shape models and boundary reconstructions. These implementations generally improved segmentation results. When integrating attention mechanisms, the soft attention strategies proposed by [PERSON] et al. (2019) [37], [PERSON] et al. (2023) [24], [PERSON] et al. (2022) [36], and [PERSON] et al. (2022) [30] improved segmentation outcomes, with DSC scores above 0.92, likely due to the high number of included patients. A low number of patients often results in poor segmentation generalizability, as the network cannot capture the prostate's diversity across different patients. This issue is evident in [PERSON] et al. 's (2022) [33] performance. However, [PERSON] et al. (2018) [20] achieved a DSC score of 0.93 with an architecture trained on just 18 patients, contradicting this hypothesis. [PERSON] et al. (2022) [36] and [PERSON] et al. (2021) [35] emphasized the importance of patient heterogeneity in datasets. Supervised Domain Adaptation (sDA) has proven valuable for improving DL architectures' generalizability when trained on a small number of patients.